## 4 Discussion
This aspect is particularly relevant, considering that the RST technique for volcanological applications (e.g., \(\mathrm{RST}_{\mathrm{VOLC}}\) algorithm) was previously implemented on high temporal (low spatial) resolution satellite data, for which statistical analyses can be based on a highly populated dataset. Hence, this work demonstrates that RST is also capable of providing reliable results using mid-high spatial resolution satellite data and in the presence of a less populated dataset (as for ASTER). It should be stressed that, in this case, we did not implement the iterative pre-process used by RST to remove signal outliers (e.g., extremely hot/cold pixels) [24] within GEE. Nonetheless, RASTer was capable of performing an accurate mapping of thermal anomalies, as shown in Figure 10 in reference to the ASTER scene of 17 October 2016 at 09:59 UTC. In detail, Figure 10a shows the identification of some anomalous pixels at BN-VOR over the Mt. Etna crater area. This thermal anomaly detection integrated information retrieved from the Sentinel-2 MSI scene of the same day at 09:40 UTC, provided by the NHI (Normalized Hotspot Indices) algorithm through the GEE-based tool [20]. The latter, indeed, flagged some hot pixels over SEC (Southeast Crater-New Southeast Crater complex), (see blue pixels in Figure 10b), but did not provide information about the BN-VOR thermal activity detected by RASTer (red pixels). This thermal activity is probably associated with the presence of incandescent material under the fresh lava, which was independently observed  
Figure 9: Time series of hot pixels flagged by RASTer at Mt. Etna in the period May 2000–2020 and different intensity levels of Mt. Etna thermal activity (colors from yellow to red). on the ground a few days before the ASTER overpass (e.g., [52]). Differences in thermal anomaly identification are ascribable to the known limitations (e.g., missed detections in presence of degassing plumes [24]) and advantages (mainly associated with the use of satellite data at higher spatial resolution) of the NHI algorithm, which exploits NIR-SWIR Sentinel-2/MSI observations, when compared with the RST-based approach applied here to ASTER TIR data. The possible complementarity between RASTer and NHI systems is further enhanced in the temporal domain, as shown in Figure 11 in reference to the Mt. Etna Strombolian activity of 14-17 January 2021 where the integration of ASTER and Sentinel-2 observations allowed for a more continuous monitoring of thermal volcanic activity. This integration can also be usefully extended to other sensors (e.g., NHI runs on Landsat-8/OLI data [20; 21]),  
Figure 10: (**a**) ASTER TIR image 17 October 2016 at 09:54 UTC covering Mt. Etna area; (**b**) thermal anomalies detected by RASTer (red pixels) and NHI\({}_{\text{SWIR}}\) ([20]) on Sentinel-2/MSI data of 09:40 UTC (blue pixels) overlapped to the topographic map of Mt. Etna. further increasing the temporal continuity of active volcanoes monitoring that, for the high-latitude regions, can today reach a mean temporal coverage up to 2 days (Figure 12). In addition, RASTer could be profitably used to assess information from systems monitoring volcanoes in near real time (using satellite data at lower spatial resolution), apart from areas (e.g., equatorial zones) where the scarcity and the bad quality of ASTER images do not enable the full RASTer implementation. Among those systems, the ones sharing the same RST-based approach, applied to data from polar (e.g., [2,14,30]) and geostationary (e.g., [53]) meteorological satellite sensors, will benefit from a RASTer-based  
Figure 11: Mt. Etna activity detected by NHI through the indices NHI\({}_{\text{SWNIR}}\) and NHI\({}_{\text{SWIR}}\) (the latter is used to detect mid-low intensity hotspots [20]) on Sentinel-2/MSI data of 14 and 17 January 2021 (left and right panels) and by RASTer on ASTER scene of 16 January 2021 (middle panel).