Soil moisture (SM) plays a fundamental role in the terrestrial water cycle and in agriculture, with key applications such as the monitoring of crop growing and hydrogeological management. In this study, a calibration procedure was applied to estimate SM based on the integration of in situ and airborne thermal remote sensing data. To this aim, on April 2018, two airborne campaigns were carried out with the TASI-600 multispectral thermal sensor on the Petacciato (Molise, Italy) area. Simultaneously, soil samples were collected in different agricultural fields of the study area to determine their moisture content and the granulometric composition. A WorldView 2 high-resolution visible-near infrared (VNIR) multispectral satellite image was acquired to calculate the albedo of the study area to be used together with the TASI images for the estimation of the apparent thermal inertia (ATI). Results show a good correlation (R² = 0.62) between the estimated ATI and the SM of the soil samples measured in the laboratory. The proposed methodology has allowed us to obtain a SM map for bare and scarcely vegetated soils in a wide agricultural area in Italy which concerns cyclical hydrogeological instability phenomena.

This study introduces a methodology for the improvement of the visibility of archaeological features using an open-source probabilistic machine learning framework applied to UAV LiDAR data from the Torre Castiglione site in Apulia, Italy. By leveraging a Random Forest classification algorithm embedded in an open-source software, the approach processes dense LiDAR point clouds to segment out vegetation from the ground and the structures. Key steps include training the classifier, generating digital terrain models, digital feature models, and digital surface models, and enhancing the visibility of archaeological features. This method has proven effective in improving the interpretation of archaeological sites, revealing previously hidden or difficult-to-access microtopographic and structural details, such as the defensive structures, terraces, and ancient paths of the Torre Castiglione site. The results underline this methodology’s ease of use in uncovering archaeological landscapes under a dense canopy. Moreover, the study emphasises the benefits of using open-source tools to enhance the documentation and analysis of remote or difficult archaeological sites.

Soil monitoring is increasingly important for environmental sustainability and biodiversity protection, as urbanization and industrial development expose fertile land to pollution risks. Soil quality can be assessed through physical–chemical parameters and by analyzing land use evolution, highlighting the need for integrated procedures that jointly address land use transitions and soil health. This study assesses temporal soil changes (2004–2019) in an area affected by impactful industrial activities (Tito Scalo, Southern Italy), alongside major land use transformations. We explore the potential of a procedure based on a combination of standard techniques: analysis of land use/cover changes, magnetic susceptibility measurements from two field surveys (2004 and 2019) as a proxy for heavy metal concentrations, and ²²⁶Ra soil concentration measurements, proving to be a decisive factor in better interpreting the evolution of magnetic susceptibility values. Results show that, despite increased industrial activities and expanded sealed areas, magnetic susceptibility values decrease in the second survey. This suggests that policy measures, including temporary suspension of certain industrial activities and remediation efforts, positively influenced environmental quality. The proposed procedure is low-cost, time-efficient, and independent of local geological, bioclimatic, or socio-economic conditions, making it suitable for monitoring areas under high anthropogenic pressure and for evaluating the effectiveness of remediation and recovery strategies supporting mitigation or remediation actions and enabling functional redevelopment of remediated sites.