Remote sensing can represent an important instrument for monitoring landfills and their evolution over time. In general, remote sensing can offer a global and rapid view of the Earth’s surface. Thanks to a wide variety of heterogeneous sensors, it can provide high-level information, making it a useful technology for many applications. The main purpose of this paper is to provide a review of relevant methods based on remote sensing for landfill identification and monitoring. The methods found in the literature make use of measurements acquired from both multi-spectral and radar sensors and exploit vegetation indexes, land surface temperature, and backscatter information, either separately or in combination. Moreover, additional information can be provided by atmospheric sounders able to detect gas emissions (e.g., methane) and hyperspectral sensors. In order to provide a comprehensive overview of the full potential of Earth observation data for landfill monitoring, this article also provides applications of the main procedures presented to selected test sites. These applications highlight the potentialities of satellite-borne sensors for improving the detection and delimitation of landfills and enhancing the evaluation of waste disposal effects on environmental health. The results revealed that a single-sensor-based analysis can provide significant information on the landfill evolution. However, a data fusion approach that incorporates data acquired from heterogeneous sensors, including visible/near infrared, thermal infrared, and synthetic aperture radar (SAR), can result in a more effective instrument to fully support the monitoring of landfills and their effect on the surrounding area. In particular, the results show that a synergistic use of multispectral indexes, land surface temperature, and the backscatter coefficient retrieved from SAR sensors can improve the sensitivity to changes in the spatial geometry of the considered site.

Accurate automatic volcanic cloud detection by means of satellite data is a challenging task and is of great concern for both the scientific community and aviation stakeholders due to well-known issues generated by strong eruption events in relation to aviation safety and health impacts. In this context, machine learning techniques applied to satellite data acquired from recent spaceborne sensors have shown promising results in the last few years. This work focuses on the application of a neural-network-based model to Sentinel-3 SLSTR (Sea and Land Surface Temperature Radiometer) daytime products in order to detect volcanic ash plumes generated by the 2019 Raikoke eruption. A classification of meteorological clouds and of other surfaces comprising the scene is also carried out. The neural network has been trained with MODIS (Moderate Resolution Imaging Spectroradiometer) daytime imagery collected during the 2010 Eyjafjallajökull eruption. The similar acquisition channels of SLSTR and MODIS sensors and the comparable latitudes of the eruptions permit an extension of the approach to SLSTR, thereby overcoming the lack in Sentinel-3 products collected in previous mid- to high-latitude eruptions. The results show that the neural network model is able to detect volcanic ash with good accuracy if compared to RGB visual inspection and BTD (brightness temperature difference) procedures. Moreover, the comparison between the ash cloud obtained by the neural network (NN) and a plume mask manually generated for the specific SLSTR images considered shows significant agreement, with an F-measure of around 0.7. Thus, the proposed approach allows for an automatic image classification during eruption events, and it is also considerably faster than time-consuming manual algorithms. Furthermore, the whole image classification indicates the overall reliability of the algorithm, particularly for recognition and discrimination between volcanic clouds and other objects.

The need for enhancing image spatial resolution has motivated the researchers to propose numerous super-resolution techniques, including those developed specifically for hyperspectral data. Despite significant advancements in this field attributed to deep learning, little attention has been given to evaluating the practical value of super-resolved images in specific applications. Most methods are validated in application-independent scenarios, often using simulated low-resolution images, resulting in overly optimistic conclusions. In this paper, we propose task-based evaluation strategies for hyperspectral image super-resolution and we present results obtained with various approaches that include pansharpening, multispectral–hyperspectral data fusion, and single-image super-resolution. We demonstrate that the proposed framework allows us to highlight both benefits and limitations of each method and can therefore guide the development of super-resolution techniques suitable for real-world applications.

The implications of the COVID-19 outbreak are subjected to an increasing number of studies. So far, air quality trends related to the lockdown due to the pandemic have been analysed in large cities or entire regions. In this work, the region studied is the metropolitan area of Cagliari, which is the main city on the island of Sardinia (Italy) and can be representative of a coastal city that includes industrial settlements. The purpose of the study is to evaluate the effect of restrictions related to the COVID-19 outbreak on air quality levels and the traffic dynamics in this type of urban area. Nitrogen Dioxide (NO₂) levels before, during and after COVID-19 lockdown have been investigated using data acquired from the Sentinel-5P/TROPOMI satellite combined with on-site measurements. Both TROPOMI detected and ground-based data have revealed higher levels of NO₂ before and after the lockdown, compared to those during the period of COVID-related restrictions, in particular in the urban area of Cagliari. On the other hand, NO₂ registered in the oil refinery area did not show significant differences associated with lockdown. The correlation of TROPOMI NO₂ tropospheric column with ground data (surface NO₂) on a monthly mean basis showed different values based on the background and the highest Pearson's coefficient was of about 0.78 near to the city centre, where traffic can be considered a significant source of emission. In addition, a comparison of the air pollution level with the dynamics of vehicle traffic was investigated. The study highlighted a remarkable correlation between the reduction of the number of vehicles and the corresponding tropospheric NO₂ values that decreased on a weekly mean basis.