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.

The frequency of wildfires is increasing worldwide, contributing to a third of forest loss in the last two decades. Tracking burned area progression using traditional optical remote sensing is hindered by cloud and smoke coverage. Therefore, this research employs multi-temporal Synthetic Aperture Radar (SAR) satellite data, which are not susceptible to atmospheric effects. Focusing on four Greek wildfires in 2021, the research utilizes unsupervised k-means clustering on bitemporal and multi-temporal SAR polarimetric features. The impact of input feature smoothing with varying moving kernel window sizes was assessed on improving accuracy. The use of these smoothed features led to a substantial improvement in accuracy across all four areas examined, while a window size of 19x19 was chosen as the right balance between preserving fine details and minimizing speckle. Furthermore, adding a filter after clustering to remove areas smaller than 2 hectares led to additional improvements in accuracy, especially in commission error. The results using the defined settings revealed F1 scores of 0.75-0.88, overall accuracy of 81-94%, and omission/commission errors of 33-16% and 14-3%, respectively. Challenges were observed in regions characterized by a substantial share of agricultural areas, while terrain effects revealed no substantial effects on the results. The assumption that the SAR will be sensitive mainly on bigger structural changes was proved in the visual validation using high-resolution imagery. Additionally, a Google Earth Engine (GEE) toolbox ‘Sentinel-1 Burned Area Progression’ (S1-BAP) was developed using the presented methodology and is freely available for the scientific community on GitHub.

Soil moisture retrievals based on rigorous physical backscattering models require a comprehensive description of the vegetation structure and biophysical parameters, including the density of the scatters, height, and vegetation water content. Semi-physical models such as the water cloud model (WCM), are also extensively used and rely on estimates of vegetation water content or biomass derived from optical vegetation indices such as LAI and NDVI. However, such indices only contain parts of the true variability of vegetation structure and how it changes across various land cover types. In this study, we introduce RTNet (Radiative Transfer Neural Network), which combines a parameterized first-order Radiative Transfer (RT) model with four scattering components (surface, volume, double-bounce, and triple-bounce scattering components) and deep residual neural networks for the soil moisture retrieval. The input features consist of the HV backscattering coefficient, vegetation water content, and several other information categories such as soil texture and weather data. The RTNet is optimized to minimize the difference between the estimated and measured HH total backscattering. After imposing a physical constraint to the RTNet outputs, they are then applied to the ensemble random forest machine learning regressor to retrieve the volumetric soil moisture. The proposed framework is validated using the SMAPVEX12 L-band UAVSAR data, aggregated to a resolution of 100 meters, which is finer than the NISAR Level 3 soil moisture product (200 meters resolution). The estimated HH total backscattering coefficients show a high agreement with the UAVSAR measured HH backscattering with a root mean square error (RMSE) of approximately 3 dB across the entire image in non-forested regions. The retrieved volumetric soil moisture also shows a very high agreement with the in-situ soil moisture achieving the RMSE of 5.65% and R² of 0.7.

In the present study, the research activities aiming at investigating the use of modern Machine Learning algorithms for the early detection of convective systems are shown.The study conceived and carried out at the Italian Air-Force Meteorological Centre, co-funded in the framework of the EUMETNET-SRNWP-EPS Project, focuses on the post-processing of NWP model output to provide to the forecasters an improved Decision Support System specifically designed for aviation hazards and severe weather phenomena, such as thunderstorms. In detail, the NWP fields generated from operational Limited Area Models (COSMO-IT, 2.2 km horizontal resolution) running at CNMCA (Centro Nazionale Meteorologia e Climatologia Aerospaziale, Italian AirForce Met Service) and ECMWF, were processed and fed, as input features, to a properly designed decision tree. Different targets were defined to train the ML model, starting from independent observation datasets, which include radar, lightning and satellite-derived observations. The products mentioned above and the input features were co-located in space and time to assess the correlation between the signal (the information content of NWP products) and the weather phenomenon (e.g., convective instability). The ML tool operates in near real-time mode for the prediction/test phase and offline mode for the training/validation phase. It runs on the European Weather Cloud (EWC), the cloud-based collaboration platform for European meteorological application development. In this context, the customizable computational capabilities of the EWC allowed us to manage the extensive dataset and reduce the model training time. Then the trained models were validated by considering external observations (i.e., the METAR dataset, which provides airports' messages concerning the main meteorological phenomena and parameters observed by weather stations, such as thunderstorms. Moreover, some case studies will be shown and, as a benchmark of the potential improvement, a comparison with classical (multi-variate, static-threshold decision-tree) post-processing applications, routinely used by the Meteorological Watch Office of the Centre, is included in the evaluation study.

This letter describes a procedure based on the radiative transfer theory to calibrate the scattering contributions from the Generalized Freeman-Durden (GFD) polarimetric decomposition over corn fields. The Tor Vergata electromagnetic model (TOV) is used to simulate canonical scattering mechanisms that are compared with those obtained applying GFD to both simulated and L-band SAOCOM-1A data. The proposed method first analyzes the error between the model and the GFD applied to the simulated data. A multivariate data fitting is then performed to derive a new expression of the GFD powers, which is tested on L-band real data. The GFD volume power obtains the greatest benefit from the calibration, reducing the Root Mean Square Error (RMSE) with respect to the corresponding TOV model contribution to 0.006 in linear units. To further test the procedure, a linear regression model is used to estimate soil moisture using the calibrated GFD powers from SAOCOM-1A real data. The retrieval performance, evaluated through a Leave-One-Out (LOO) cross-validation against in situ data, shows a significant improvement: the calibrated GFD powers leads to an increased linear correlation (0.32 to 0.57), while the RMSE is reduced (0.096 to 0.055 m³/m³).