Remote detection of Thermal Anomalies at Campi Flegrei caldera via Independent Component Analysis (ICA).

Thermal InfraRed (TIR) Remote Sensing is a well-consolidated approach to detect ground thermal anomalies for geological, environmental and urban scenarios. Specifically, several methodologies have been developed for the TIR imagery analysis to retrieve the Land Surface Temperature (LST) and describe the thermal state of the Earth’s surface. In volcanic frameworks, the analysis of LST time series represents a valid tool for a fast characterization of the shallow thermal field, supporting the surveillance networks in monitoring their status, specifically for the areas inaccessible because of the high volcanic hazard.

Here, we propose a workflow to detect the thermal patterns in volcanic areas by analyzing time series of satellite TIR images using the Independent Component Analysis (ICA) technique. In particular, the first step of the workflow relies on the retrieval of LST time series from Landsat-8 (L8) TIR nighttime acquisitions, which have spatial and temporal resolutions equal to 100 m and 16 days, respectively, acceptable for our purposes. We selected the nighttime images because they allow us to reduce the exogenous effects, as well as those related to the solar radiation. Therefore, we estimate the LST parameter by considering the Radiative Transfer Equation (RTE) based on the use of a single thermal band, as long as having the surface emissivity and the atmospheric information about the investigated area. The second step of the considered workflow deals with the application of the ICA method to the retrieved LST time series to identify the statistically independent components (ICs) of the LST multivariate dataset.

We verify the robustness of the proposed workflow by analyzing the volcanic site of Campi Flegrei caldera (Southern Italy), which represents a well-suitable case study for the occurrence of several endogenous and exogenous phenomena. We first achieved the 2013 – 2022 LST time series and subsequently analyzed the four components identified by the ICA. We compare these main thermal patterns with other available independent datasets, for example, the seismicity, the ground deformation field and the depth of the water table in the area, proving: (i) the existence of a positive thermal anomaly at the Solfatara crater with endogenous nature; (ii) the occurrence of exogenous processes at the Agnano plain; (iii) the existence of peculiar climatic pattern at the Astroni crater.

In conclusion, we remark that the proposed methodology allows the identification of the nature of thermal anomalies, even for complex volcanic scenarios where several processes of different nature occur interfering with each other.