The integration of miniaturized magnetometers with Unmanned Aerial Vehicles (UAVs) has revolutionized magnetic surveying, offering flexible, high-resolution, and cost-effective solutions for geophysical applications also in remote areas. This study presents a comparative analysis of two configurations using UAV-borne scalar magnetometers through several surveys conducted in the Altopiano di Verteglia (Southern Italy), chosen as a test-site since buried pipes are present. The two systems differ significantly in sensor–platform arrangement, noise sensitivity, and flight configuration. Specifically, the first employs the MagNimbus magnetometer with two sensors rigidly attached on two masts at fixed distances, respectively, above and below the UAV, enabling the vertical gradient estimation while increasing noise due to proximity to the platform. The second involves the use of the MagArrow magnetometer suspended at 3 m below the UAV through non-rigid ropes, which benefits from minimal electromagnetic interference but suffers from oscillation-related instability. The retrieved magnetic anomaly maps effectively indicate the location and orientation of buried pipes within the studied area. Our comparative analysis emphasizes the trade-offs between the two systems: the MagNimbus-based configuration offers greater stability and operational efficiency, whereas the MagArrow-based one provides cleaner signals, which deteriorate with the vertical gradient computation. This work underscores the need to optimize UAV-magnetometer configurations based on environmental, operational, and survey-specific constraints to maximize data quality in drone-borne magnetic investigations.

In volcanic regions, the analysis of Thermal InfraRed (TIR) satellite imagery for Land Surface Temperature (LST) retrieval is a valid technique to detect ground thermal anomalies. This allows us to achieve rapid characterization of the shallow thermal field, supporting ground surveillance networks in monitoring volcanic activity. However, surface temperature can be influenced by processes of different natures, which interact and mutually interfere, making it challenging to interpret the spatio-temporal variations in the LST parameter. In this paper, we use a workflow to detect the main thermal patterns in active volcanic areas by analyzing the Independent Component Analysis (ICA) results applied to satellite nighttime TIR imagery time series. We employed the proposed approach to study the surface temperature distribution at the Campi Flegrei caldera volcanic site (Southern Italy, Naples) during the 2013–2022 time interval. The results revealed the contribution of four main distinctive thermal patterns, which reflect the endogenous processes occurring at the Solfatara crater, the environmental processes affecting the Agnano plain, the unique microclimate of the Astroni crater, and the morphoclimatic aspects of the entire volcanic area.

Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system’s behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period.

Thermal infrared (TIR) remote sensing is an increasingly used technique for studying various natural and anthropogenic processes by evaluating the thermal state of the Earth’s surface. Technological advancements have supported the development of thermal cameras for ground-based, airborne, and satellite platforms. Additionally, Unmanned Aerial Systems (UAS) are increasingly regarded as versatile platforms due to their flexible observation scales.In a volcanic framework, TIR remote sensing enables the study of ground temperature and the identification of thermal anomalies caused by hot fluid discharge (e.g., gas and lava) or surface heating due to fluid migration in the subsoil during unrest phases, which modify the pressure and temperature conditions of the crust. TIR remote sensing is therefore an essential tool for monitoring and surveillance of active volcanoes, although the spatial coverage and resolution of planned surveys can sometimes be inadequate for emergency management. Indeed, ground-based measurements do not guarantee extensive spatial coverage, while satellite data lack flexibility regarding spatial and temporal resolutions. Finally, airborne measurements are challenging to organize operationally during emergencies and are inherently risky. In this scenario, UAS platforms represent a reasonable trade-off in terms of spatial coverage, resolution, and logistics.Here, we present a case study of multiplatform (satellite and UAS) TIR remote sensing as part of the monitoring activities at the Campi Flegrei caldera by INGV – OV. This active volcanic system is characterized by complex interactions between magmatic and hydrothermal reservoirs, causing frequent unrest with ground deformation, seismicity, gas emissions, and surface temperature anomalies. Among the latter, we focus on the most significant anomalies located near the Solfatara – Pisciarelli hydrothermal system.Satellite measurements consist of nighttime images acquired by the Landsat-8 and Landsat-9 satellites from May 2018 to August 2025, with a 100 m spatial resolution, processed to retrieve an approximately monthly distribution of Land Surface Temperature (LST). Conversely, UAS data consist of images acquired monthly by INGV – OV with a 10 cm spatial resolution at flight altitudes ranging from 45 to 70 m. For logistical reasons, the Pisciarelli dataset spans from September 2019 to May 2025, while images of Solfatara were only acquired during the first halves of 2024 and 2025.The results show that satellite data can detect a single anomaly at the Solfatara – Pisciarelli hydrothermal system without revealing significant temporal variations in temperature. On the other hand, UAS data identify multiple anomalies for both the Solfatara and Pisciarelli sites, highlighting surface heating in Pisciarelli starting around September 2021. This trend is consistent with analyzed seismicity and ground deformation datasets.This study demonstrates the role of multiplatform TIR data integration in improving monitoring and surveillance activities at active volcanoes.

In volcanic regions, Thermal InfraRed (TIR) remote sensing is a well-established technique for detecting ground thermal anomalies. The analysis of thermal properties, particularly of Land Surface Temperature (LST) time series, represents a valid tool to achieve a rapid characterization of the shallow thermal field, supporting ground-based surveillance networks in the monitoring of volcanic activity, especially in areas that are inaccessible due to high volcanic hazard.However, in complex active volcanic and hydrothermal settings, the coexistence of processes of different natures that interact and mutually interfere can significantly affect the distribution of the LST parameter, making it challenging to interpret its spatio-temporal variations. In this context, the extraction of the main thermal patterns of volcanic areas from satellite-derived LST time series represents a further step for a more detailed characterization of the shallow thermal field.In this study, the extraction of the main thermal patterns from satellite-derived LST time series is addressed through decomposition techniques such as the Independent Component Analysis (ICA) and the Dynamic Mode Decomposition (DMD). ICA is a statistical method aimed at identifying a linear transformation of the data that maximizes the statistical independence between its components, defining the signal’s independent components (ICs). DMD is a data-driven technique aimed at decomposing spatio-temporal data for the extraction of coherent features, defining a set of dominant dynamic modes (DMs). The investigated area is the Campi Flegrei caldera (southern Italy), a well-known complex volcanic system. The LST time series is retrieved from cloud-free nighttime TIR images acquired by Landsat-8 and Landsat-9 missions (L8 and L9) during the 2018–2025 time interval. Specifically, the LST parameter is estimated through the Radiative Transfer Equation (RTE) applied to a single thermal band (Band 10 for both L8 and L9) and with known information on the surface emissivity and atmospheric conditions of the investigated area. Subsequently, the application of ICA and DMD methods allowed the identification of the main components, revealing the dominant thermal patterns influencing the LST distribution and providing insights into the endogenous and exogenous processes characterizing the volcanic site.