In this study, Synthetic Aperture Radar (SAR) methods are combined with optical ones for earthquake damage assessment after the 2023 Turkey earthquake. First, a change detection scheme, that exploits both SAR and optical images is applied to detect and classify the damaged buildings. Then, the SAR and optical data are combined using a data fusion step based on the k-means algorithm, to improve the reliability and accuracy of the damage assessment map. Results for the city of Kahramanmaras, and undertaken over images collected before and after the event with the X-band COSMO-SkyMed-Second Generation (CSG) SAR sensor and PLANETSCOPE (PLTS) optical sensor rely on demonstrating the effectiveness of the joint use of SAR and optical change detection methods in detecting damage to buildings and infrastructure that can be used for disaster management authorities to prioritize rescue and relief operations in the affected regions.

The forthcoming Surface Biology and Geology thermal mission, developed by ASI (Agenzia Spaziale Italiana) and NASA-Jet Propulsion Laboratory, will launch in orbit a payload featuring a MWIR-LWIR sensor and a VNIR camera. To date, no operational mission has employed more than one channel in the mid-infrared region with a spatial resolution comparable to that of SBG-TIR. Here we exploit the potential of the SBG-TIR sensor for thermal anomalies detection and characterization in a volcanic environment.

Effusive eruptions are the most common volcanic manifestations in basaltic volcanoes. Emission of lava flows may occur from the main craters at summit or from vents along the flanks, representing constant threat to the human infrastructures located in volcanic areas [1] . The motion of lava flows are dependent on the physical and chemical characteristics of lava [2] and their extensions are strongly controlled by the rate at which lava is emitted at the vents [3] , [4] . However, complex eruptive dynamics, linked with the sequential opening of new vents and fluctuations in effusion rates may lead to the development of composite lava fields. Furthermore, the formation of thermally-insulated systems such as lava tubes, allows the flow to reach a greater extent due to the lower cooling rate. The development of complex tube networks is highlighted by the presence of different orders of ephemeral vents along the margins of the main flow, which is controlled mainly by the ground slope and the discharge rates [5] .

Monitoring of active volcanoes, by combining field observations, satellite data and numerical models, presents extraordinarily challenging problems, from detecting and quantifying hazardous phenomena during eruptive events to forecasting their impact to assess risks to people and property. At INGV Osservatorio Etneo in Catania, the CL-HOTSAT system [1] was designed and implemented in order to follow the manifestation of volcanic activity by using data from spaceborne thermal systems such as those acquired by MODIS, VIIRS, SLSTR, and SEVIRI sensors. Among these, the geostationary SEVIRI was able to track the evolution of short-lived events such as the lava fountains occurring at Mt Etna [2] or the paroxysmal events in Stromboli [3] . Anyway, because of the low spatial resolution of SEVIRI images (3 km at nadir, sub-satellite point at the center of the disc), most of the lower intensity thermal activity, such as those related to strombolian activity, spattering or small lava flows could not be detected. Another issue is that in case of intense eruptive activity the SEVIRI channel 4 (3.9 µm), useful to observe high temperature features, often saturates, causing a possible underestimation in the recorded signal.

Satellite remote sensing is playing an increasing role in volcano monitoring, not only at little-known and remote volcanoes where in-situ data are often unavailable, but also at well-monitored volcanoes to complement ground-based measurements. The synoptic view captured by multi-source satellite imagery over volcanoes can benefit hazard monitoring efforts, both following the different phases and intensities of an eruption, as well as helping in nowcasting and eventually forecasting the areas potentially threatened by hazardous phenomena. Here we demonstrate how satellite data from different sources can be combined to refine the satellite-derived radiant heat flux and time-averaged discharge rates (TADRs) estimation, and to improve real-time monitoring and nowcasting of eruptive events. This is achieved by cross-referencing TADRs obtained from low spatial/high temporal resolution satellite data (e.g. MODIS, SEVIRI, VIIRS, SLSTR) with maps of volcanic deposits including the time-varying evolution of lava flow emplacement derived from medium spatial resolution multispectral satellite data (e.g. EO-ALI, Landsat, Sentinel-2, ASTER), and with detailed three-dimensional maps of volcanic deposits retrieved from the topographic monitoring by using stereo or tri-stereo optical data (e.g. Pleiades, PlanetScope, SkySat, ASTER). Finally, satellite-derived parameters are used as input and validation for the numerical modelling of lava flow scenarios. This strategy is here applied to recent eruptions occurred on Etna, Stromboli and Nabro volcanoes.

The past few decades have seen an explosion of satellite remote sensing techniques for the monitoring of volcanic thermal features. Here, we propose an artificial neural network approach for improving the cloud detection through imperfect multispectral satellite images analysis. The cloud detection algorithm has been tested on a data set of MSG-SEVIRI images acquired over the area of Etna volcano in Sicily (Italy) before and during the 2008 eruption. Results show that this approach is robust in terms of percentage of correctly classified pixels.