In this study, a methodology is proposed to use dualpolarimetric Synthetic Aperture Radar (SAR) to identify the spatial distribution of soil liquefaction. The latter is a phenomenon that occurs in conjunction with seismic events of a magnitude generally higher than 5.5-6.0 and which affects loose sandy soils located below the water table level. The methodology consists of two steps: firstly the spatial distributions of soil liquefaction is estimated using a constant false alarm rate (CFAR) method applied to the SPAN metric, namely the total power associated to the measured polarimetric channels, which is ingested into a bi-temporal approach to sort out dark areas not genuine. Secondly, the obtained masks are read in terms of the physical scattering mechanisms using a child parameter stemming from the eigendecomposition of the covariance matrix - namely the degree of polarization. The latter is evaluated using the co-seismic scenes and contrasted with the pre-seismic one to have rough information on the time-variability of the scattering mechanisms occurred in the area affected by soil liquefaction. Finally, the obtained maps are qualitatively contrasted against state-of-theart optical and interferometric SAR methodologies. Experimental results, obtained processing a time-series of ascending and descending Sentinel-1 SAR scenes acquired during the 2023 Turkey-Syria earthquake, confirm the soundness of the proposed approach.

This study is to describe the activities carried on in the frame of the research project Multi-layEr approaCh to detect and analyze cOastal aggregation of MAcRo-plastic littEr (ECO-MARE). The research activity, funded by the Italian Ministry of Research (MUR), is to observe aggregation of macroplastic at sea using measurements remotely sensed by a suite of sensors equipped onboard of tower-, drone-, and satellite platforms. In this study, the design of the research activity is described and a preliminary experimental campaign will be presented.

This study proposes a feature-fusion framework that jointly takes advantage of single-polarization (SP) and quad-polarization (QP) synthetic-aperture radar (SAR) imagery for reliable detection of marine oil spills and distinguishing it from look-alike phenomena. By blending complementary polarimetric properties and textural cues at the feature level, the framework markedly reduces false-alarm rates. To facilitate fair, comparable assessments, the work introduces the Quad-Polarization Oil Spill Detection (QPOSD) dataset, a fully annotated suite of QP SAR scenes, this also alleviates the scene selection bias that has long constrained QP investigations. For demonstration, the feature-fusion method is instantiated with a lightweight channel-wise concatenated multilayer network (CWCM-Net) and trained in a parallel way on the SP and QP data. The resulting model achieves an overall accuracy of 99.02% while substantially reducing false positives, underscoring the effectiveness of the proposed feature fusion approach and the practical value of the new QPOSD dataset for large-scale, operational oil spill monitoring.

This study is to discuss marine speckle in near-coincident C-and X-band Synthetic Aperture Radar (SAR) measurements. The aim is to investigate the changes in the speckle statistics under the same marine scene with given met-ocean conditions using SAR imagery collected at different frequencies and spatial resolutions. Experimental results are showcased using Sentinel-1 single-look complex (SLC) VV-polarized and Cosmo-SkyMed SLC HH-polarized SAR scenes collected under low-to-moderate wind regime, with the aim of analyzing the different behavior of the speckle signal as a function of spatial resolution.

This study addresses the observation of floating plastic debris in freshwater environments using an Unmanned Aerial Vehicle (UAV) multi-sensor strategy. An experimental campaign is described where an heterogeneous plastic assemblage, namely a plastic target, and a naturally occurring leaf-litter mat are observed by a UAV platform in the Lake Calore (Avellino, Southern Italy) within the framework of the “multi-layEr approaCh to detect and analyze cOastal aggregation of MAcRo-plastic littEr” (ECOMARE) Italian Ministry of Research (MUR)-funded project. Three UAV platforms, equipped with optical, multispectral, and thermal sensors, are adopted, which overpass the two targets with the objective of analyzing the sensitivity of optical radiation to plastic and the possibility of discriminating the plastic target from the natural one. Georeferenced orthomosaics are generated across the visible, multispectral (Green, Red, Red Edge, Near-Infrared—NIR), and thermal bands. Two novel indices, the Plastic Detection Index (PDI) and the Heterogeneity Plastic Index (HPI), are proposed to discriminate between the detection of plastic litter and natural targets. The experimental results highlight that plastics exhibit heterogeneous spectral and thermal responses, whereas natural debris showed more homogeneous signatures. Green and Red bands outperform NIR for plastic detection under freshwater conditions, while thermal imagery reveals distinct emissivity variations among plastic items. This outcome is mainly explained by the strong NIR absorption of water, the wetting of plastic surfaces, and the lower sensitivity of the Mavic 3′s NIR sensor under high-irradiance conditions. The integration of optical, multispectral, and thermal data demonstrate the robustness of UAV-based approaches for distinguishing anthropogenic litter from natural materials. Overall, the findings underscore the potential of UAV-mounted remote sensing as a cost-effective and scalable tool for the high-resolution monitoring of plastic pollution over inland waters.

This study is to summarize the lesson learned during the July 2024 experimental campaign where targets, consisting of floating plastic and natural debris, were jointly observed in a South Italy lake by drone and satellite instruments. The satellite layer consists of the X-band Synthetic Aperture Radar (SAR) equipped onboard the Italian Cosmo-SkyMed Second Generation (CSG) constellation, while the drone layer consists of an optical camera. A set of multi-polarization features, namely the co- and cross-polarized normalized radar cross section (NRCS), the SPAN and the like-to-cross-polarization ratio, are extracted from the CSG SAR measurements. Their sensitivity to the plastic target, along with their ability to discriminate plastic from natural floating debris, are investigated using objective metrics. Experimental results, obtained under calm water conditions, show that floating plastic calls for co- and cross-polarized signals well-above the system noise and well-distinguishable from the surrounding water using all the features. The plastic target appears as a spot brighter than the surrounding water in both the co- and the cross-polarized NRCS imagery. The co-polarized NRCS calls for the largest plastic-to-water contrast and the largest separation between the plastic and the natural target. However, this separation is such that the discrimination is a challenging task when using intensity-based dual-polarimetric features.