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.

Twenty-one years of ASTER global thermal infrared (TIR) acquisitions provide a large amount of data for volcano monitoring. These data, with high spatial and spectral resolution, enable routine investigations of volcanoes in remote and inaccessible regions, including those with no ground-based monitoring. However, the dataset is too large to be manually analyzed on a global basis. Here, we systematically process the data over several volcanoes using a deep learning algorithm to automatically extract volcanic thermal anomalies. We explore the application of a Convolutional Neural Network (CNN), specifically UNET, to detect subtle to intense anomalies exploiting the spatial relationships of the volcanic features. We employ a supervised UNET network trained with the largest (1500) labeled dataset of ASTER TIR images from five different volcanoes, namely Etna (Italy), Popocatépetl (Mexico), Lascar (Chile), Fuego (Guatemala), and Kliuchevskoi (Russia). We show that our approach achieves high accuracy (93%) with excellent generalization capabilities. The effectiveness of our model for detecting the full range of thermal emission is shown for volcanoes with very different styles of activity and tested at Vulcano (Italy). The results demonstrate the potential applicability of the proposed approach to the development of automated thermal analysis systems at the global-scale using future TIR data such as the planned NASA SBG mission.

Vulcano Fossa's fumarole field (Italy) has been active for more than a century and has become a well‐studied benchmark for fumarolic degassing, often being considered the “model” hydrothermal system. Satellite thermal monitoring is increasingly being used to monitor such systems, so we here use Vulcano to test a new method for assessing heat flux at such systems. Our methodology involves converting ground‐based vent temperature measurements to heat fluxes emitted by the fumaroles, with the diffuse heat flux obtained from satellite‐sensor (in our case Advanced Spaceborne Thermal Emission and Reflection Radiometer) data. While diffuse heat losses were typically 9 MW, vent heat losses were 1 MW. The average total flux of 10 MW over the 19‐year period of study places Vulcano in the top 20 most active hydrothermal systems globally. This work highlights the value of high spatial resolution infrared satellite data in building thermal inventories for persistently active hydrothermal systems.

Shiveluch (Kamchatka, Russia) is the most active andesitic volcano of the Kuril-Kamchatka arc, typically exhibiting near-continual high-temperature fumarolic activity and periods of exogenous lava dome emplacement punctuated by discrete large explosive eruptions. These eruptions can produce large pyroclastic flow (PF) deposits, which are common on the southern flank of the volcano. Since 2000, six explosive eruptions have occurred that generated ash fall and PF deposits. Over this same time period, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument has been acquiring image-based visible/near infrared (VNIR), short wave infrared (SWIR) and thermal infrared (TIR) data globally, with a particular emphasis on active volcanoes. Shiveluch was selected as an ASTER target of interest early in the mission because of its frequent activity and potential impact to northern Pacific air transportation. The north Pacific ASTER archive was queried for Shiveluch data and we present results from 2000 to 2009 that documents three large PF deposits emplaced on 19 May 2001, 9 May 2004, and 28 February 2005. The long-term archive of infrared data provides an excellent record on the changing activity and eruption state of the volcano.

Active volcanism was (and potentially still is) an important process that shapes the Venus surface and its detection is a primary goal for the planned VERITAS and EnVision missions. Therefore, understanding lava flow emplacement and timing on Venus is important. We adapt the terrestrial PyFLOWGO thermorheological model to Venus conditions to assess the effects on channelized lava flow propagation. We first initiate the model with terrestrial basaltic parameters and progressively adapt it to Venusian conditions in five steps: (a) gravity, (b) ambient atmospheric temperature, (c) specific heat capacity and wind speed, (d) atmospheric density, and (e) coupled convective and radiative heat flux. Compared to Earth, the slightly lower gravity on Venus resulted in a lower flow velocity, a higher crust coverage, and a very minor increase in flow length (0.1%). Increasing the ambient atmospheric temperature reduced heat loss and produced a (77%) longer flow; whereas next accounting for the atmospheric specific heat capacity and wind speed increased the flow length slightly more (81%). However, increasing the atmospheric density resulted in a shorter lava flow (13%) due to more efficient cooling. Finally, accounting for coupled convective and radiative heat loss due to the strong CO ₂ infrared absorption resulted in an increase of the flow length (∼75%). Although the model applies only to channelized, cooling‐limited flows, these results reveal that for the same effective effusion rate and topography, a Venusian lava flow travels a longer distance than the equivalent flow on Earth and its cooling should be detectable by future orbital instruments.

On 3 June 2018, Fuego volcano experienced a VEI = 3 eruption, which produced a pyroclastic density current (PDC) that devastated the La Réunion resort and the community of Los Lotes, resulting in over 100 deaths. To evaluate the potential hazard to the population centers surrounding Fuego associated with future PDC emplacement, we used an integrated remote sensing and flow modeling-based approach. The predominate PDC travel direction over the past 15 years was investigated using thermal infrared (TIR) data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument validated with ground reports from the National Institute of Seismology, Volcanology, Meteorology, and Hydrology (INSIVUMEH), the government agency responsible for monitoring. Two different ASTER-derived digital elevation model (DEM) products with varying levels of noise were also used to assess the uncertainty in the VolcFlow model results. Our findings indicate that the recent historical PDC travel direction is dominantly toward the south and southwest. Population centers in this region of Fuego that are within ~2 km of one of the volcano’s radial barrancas are at the highest risk during future large eruptions that produce PDCs. The ASTER global DEM (GDEM) product has the least random noise and where used with the VolcFlow model, had a significant improvement on its accuracy. Results produced longer flow runout distances and therefore better conveys a more accurate perception of risk. Different PDC volumes were then modeled using the GDEM and VolcFlow to determine potential inundation areas in relation to local communities.