Socio-economic and human activities represent the major impacts on marine pollution affecting the marine ecosystems, raising increasing scientific, societal and policy interest due to the long-term consequences for the environment and the coastal economies.Especially in semi-enclosed basins such as the Mediterranean Sea, the accumulation of marine litter, and particularly plastic items, represents a serious issue, intensified by intense anthropogenic pressure, high coastal population density and circulation patterns.The identification of plastic litter sources and their tracking on the sea surface represents a first step to identify the most vulnerable areas and accumulation hotspots.To achieve this goal, the hydrodynamic Lagrangian model Track Marine Plastic Debris (TrackMPD) was applied in the strait of Sicily considering rivers as input sources of plastic litter. The area is characterized by the presence of important hydrodynamic processes, including sea currents, mesoscale eddies, upwelling events etc. The quantities of plastic items to be released by rivers into the marine environment were estimated considering the data provided by “The Ocean Cleanup” website.The model’s results were compared with the in-situ data collected during the monitoring campaign conducted by “Agenzia Regionale per la Protezione ambientale” (ARPA) and “Consiglio Nazionale delle Ricerche” (CNR) in August 2018 and 2019, where microplastic items were sampled using a Manta net along fixed transects.Since the actual discharging point and period of the debris recorded by ARPA-CNR were unknown, the comparison was performed by considering 105 scenarios, each characterized by a different starting day of the daily particle release. The scenarios span a temporal interval from May up to the sampling day; for each subsequent scenario, the discharging time window progressively decreases as the release start date is shifted forward by one day.The accumulation and density maps were realized for each scenario. A buffer area was considered around each transect and the sum of the simulated particles within it was calculated, comparing the results with the microplastics sampled. The comparison between the simulations and the in-situ campaigns was performed by computing the coefficient of determination, R².The results highlight the difficulties validating the hydrodynamic model by using the in-situ data, indeed very low R2 values were performed for each scenario.Further developments of this work include the determination of realistic scenarios of plastic discharge into the marine environment through a detailed study of the territory, considering local productive activities and the resulting pollutant generation, coupled with targeted sampling campaigns to provide data for model implementation and/or validation.

This study presents a scalable cloud-based workflow for reservoir surface area and storage monitoring from Sentinel-1 SAR imagery in Google Earth Engine. The framework integrates incidence-angle filtering, supervised classification, morphological post-processing, and reservoir-specific area–volume relationships to generate consistent surface-area and storage time series. Applied to 41 reservoirs in Sicily, the workflow prioritizes IW3 acquisitions (~42–46°) to reduce angular backscatter variability and improve temporal consistency. Water detection is performed using a Support Vector Machine trained on VV and VH backscatter, providing better performance than threshold-based and alternative classifiers implemented in GEE. Validation against high-resolution PlanetScope NDWI masks showed strong agreement (R² > 0.95; KGE up to 0.96). A shoreline morphometric analysis further showed that classification accuracy is influenced by shoreline complexity, with smoother and more compact reservoirs yielding more reliable results. The proposed framework provides a transferable and operational approach for satellite driven reservoir monitoring in data-scarce environments.

Invasive alien plants threaten Mediterranean ecosystems, where early detection is essential for effective management and biodiversity conservation. Ailanthus altissima is a fast growing, highly competitive tree invader whose ecological plasticity and allelopathic properties intensify impacts on native vegetation, highlighting the need for reliable monitoring. This study develops and evaluates a remote-sensing workflow based on multitemporal PlanetScope imagery and Support Vector Machine (SVM) classification algorithm to map Ailanthus in ecologically sensitive areas. A training dataset was built in the heterogeneous urban park of “La Favorita”, characterized by extensive Ailanthus nuclei and diverse land cover types. The trained model was then applied to the “Vallone Piano della Corte” Nature Reserve in central Sicily, a sensitive area currently experiencing Ailanthus invasion. Six SVM kernel configurations were tested and validated using field surveys and UAV-based observations. Among them, Coarse Gaussian SVM provided the most balanced performance, effectively detecting the minority invasive class while reliably discriminating dominant land cover categories. Detection accuracy depended on life stage, canopy density, and spatial configuration of Ailanthus, with juvenile or sparse individuals remaining difficult to detect due to spectral mixing and radiometric smoothing due to cubic convolution resampling of PlanetScope images. The use of multitemporal imagery proved essential for leveraging phenological differences between Ailanthus and co-occurring vegetation. The final distribution map supports monitoring, control planning, and ecological restoration, and can be integrated into ecohydrological or vegetation-dynamics models to assess invasion under changing environmental conditions. Overall, the study provides an operational and transferable workflow for invasive species monitoring in Mediterranean landscapes.

Climate change increasingly affects agricultural systems, driving the need for scalable tools to support precision agriculture and decision-making. High-quality plant-scale spatial information is essential for Decision Support Systems (DSS), yet accurate canopy mapping often relies on costly sensors and complex workflows. This study proposes a YOLO-based deep learning framework for automatic detection and segmentation of citrus tree canopies using UAV-acquired RGB images. The approach leverages commercially available, low-cost sensors to balance accuracy and economic feasibility. The model is evaluated on a Mediterranean citrus orchard using datasets from UAV-mounted cameras with high and low spatial resolutions. Two YOLO architectures, YOLOv11-Large and YOLOv8-Nano, are tested under multiple scenarios to assess the combined effects of model capacity and image resolution. Results show high and consistent performance across all configurations, with mAP@50–95 exceeding 0.84. Findings demonstrate the robustness of YOLO models for canopy detection, even when training and inference use datasets with different resolutions.

Accurate estimations of actual crop evapotranspiration are essential to evaluate crop water requirements, to improve water use efficiency in agriculture, and to optimize the use of available freshwater resources. To this aim, several models were developed to allow quantifying crop water requirements based on the knowledge of actual crop evapotranspiration rates, ETa. The objective of this research was to estimate ETa using a simplified distributed model combining ground and remotely sensed data. The experiment was carried out in a Mediterranean commercial citrus orchard (C. reticulata cv. Tardivo di Ciaculli) located in the Northwest of Sicily, Italy, during the whole 2019. The experimental layout consisted of: i) a WatchDog 2000 standard weather station (measuring the main climate variables and the precipitation depths, P); ii) a database of irrigation volumes, I, scheduled by the farmer; iii) an Eddy Covariance tower equipped with an open patch gas-analyzer, a three-dimension sonic anemometer, a four-component net radiometer, and a soil heat flux plate iv) a dataset of 75 Sentinel-2 multispectral images, acquired in clear sky condition. In particular, the daily crop reference evapotranspiration, ETo, was calculated according to the FAO-56 Penman-Montheith equation using the climate variables; the crop coefficient, Kc, the Fractional Vegetation Cover, FVC, and, thus, the potential evapotranspiration, ETp, were computed via the processing of reflectance values in the RED, NIR and SWIR spectral bands. The Available Water, AW, the short-term water stress factor, Cws, and the ETa, were computed by analyzing cumulated ETp and water-supplying values using moving temporal windows characterized by different sizes (from 5 to 400 days). The validation of the model outputs was carried out by taking into account the ETa of the pixels within the flux tower footprints estimated at each satellite acquisition day (i.e. by selecting the pixels on the basis of the footprint shape and extension). The performance of the model was evaluated for each temporal window size using the following metrics: the Root Mean Square Error, RMSE, the Mean Absolute Error, MAE, the angular coefficient of the regression line forced to the origin, b, and the determination coefficient, R2. Results suggest that the best temporal window size for this crop is around 85 days allowing to achieve an RMSE of 0.51 mm d-1, a MAE of 0.38 mm d-1, a b value of 0.94 and an R2 of 0.96. The comparison with the model outputs over the whole field (all the pixels within the crop field) revealed that a strong decrease in all the metrics occurs if the validation of the remote sensing products is not properly carried out.

Climate change and water scarcity are major threats to the sustainability of wheat production in Mediterranean regions. Thus, timely and reliable water demand assessments are crucial to drive decisions on crop management strategies that are useful for agricultural adaptation to climate change challenges. Although the AquaCrop model is widely used to infer crop yields, it requires continuous field-based observations (mainly soil water content and crop coverage). Often, these areas suffer from a scarcity of in situ data, suggesting the need for remote sensing and model-based decision support. In this framework, this research intends to compare the performance of the AquaCrop model using four different input combinations, with one employing ERA5-Land and crop cover retrieved by satellite images exclusively. A field experiment was conducted on durum wheat (highly sensitive to water stress and playing a strategic role in national food security) in northwest Tunisia during the growing season of 2024-2025, where meteorological variables, green Canopy Cover (gCC), Soil Water Content (SWC), and final yields (biological and grain) were monitored. The AquaCrop model was applied. Four model input combinations were evaluated. In situ meteorological data or ERA5-Land (E5L) reanalysis were combined with either measured-gCC (measured-gCC) or Sentinel-2 NDVI-derived gCC (NDVI-gCC). The results showed that E5L reproduced temperature with RMSE < 2.4 degrees C (NSE > 0.72) and ETo with RMSE equal to 0.57 mm d(-1) (NSE = 0.79), while precipitation presented larger discrepancies (RMSE = 4.14 mm d(-1), NSE = 0.58). Sentinel-2 effectively captured gCC dynamics (RMSE = 15.65%, NSE = 0.73) and improved AquaCrop perfomance (RMSE = 5.29%, NSE = 0.93). Across all combinations, AquaCrop reproduced yields within acceptable deviations. The simulated biological yield ranged from 9.7 to 11.0 t ha(-1) compared to the observed 10.3 t ha(-1), while grain yield ranged from 3.0 to 3.5 t ha(-1) against the observed 3.3 t ha(-1). As expected, the best agreement with measured yield data was obtained using in situ meteorological data and measured-gCC, even if the use of in situ meteorological data coupled with NDVI-gCC, or E5L-based meteorological data coupled with NDVI-gCC, produced realistic estimates. These results highlight that the application of AquaCrop employing E5L and Sentinel-2 inputs is a feasible alternative for crop monitoring in data-scarce environments.

Evapotranspiration (ET) knowledge is crucial for evaluating crop field water budgets and agricultural water resources management. To monitor crop water requirements various data sources are used such as: in situ (meteorological and soil water content data) measurements, reanalysis database, remote sensing observations, and models. Two approaches can be implemented: the Soil Water Balance (SWB) and the Surface Energy Balance (SEB).This research aimed to evaluate these two approaches, by combining in situ or reanalysis meteorological data with remotely sensed images to explore the possible synergies between the approaches to propose an operational ET estimation in the context of future Thermal InfraRed (TIR) missions (TRISHNA and LSTM). With a SWB model, both actual evapotranspiration (ETa) and soil water content (SWC) were daily estimated; whereas, with a SEB model latent heat flux (LE) was instantaneously evaluated.Among the available SWBs, the SAtellite Montoring for Irrigation (SAMIR) is a FAO-2Kc-based model integrating remotely sensed images of vegetation cover for evapotranspiration spatialization and water balance. SAMIR can be forced by irrigation either measured or simulated employing specific rules based on the simulated SWC. Alternatively, the Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) is a two-source SEB model driven by remotely sensed Land Surface Temperature (LST) and vegetation cover. Both SWB and SEB were investigated by using different input variable combinations. For SAMIR, two combinations were employed: a) using in situ and b) using ERA5-Land reanalysis meteorological variables to estimate crop reference evapotranspiration and precipitation depth. Both incorporated farmer irrigation scheduling and Sentinel-2 NDVI-derived vegetation cover. For SPARSE, three combinations were employed: a) using in situ meteorological data, LST, and albedo; b) replacing LST and albedo with Landsat-8/9 data; c) replacing in situ data with ERA5-Land reanalysis while maintaining Landsat-8/9 inputs.The experiments occurred during seven irrigation seasons, from 2018 to 2024, in a Mediterranean citrus orchard (Citrus reticulata Blanco cv. Mandarino Tardivo di Ciaculli), located near Palermo, Italy (38° 4’ 53.4’’ N, 13° 25’ 8.2’’ E) in which different irrigation systems and management strategies were applied. The field was equipped with a standard weather station, an Eddy Covariance tower, and four “drill and drop” probes to acquire: meteorological variables, energy fluxes, and SWC, respectively.SAMIR best performance was obtained using the a-combination with Root Mean Square Error (RMSE) always less than 0.54 mm d-1 and 0.02 cm3 cm-3 for ETa and SWC, respectively. These metrics were achieved excluding data from 2021 during which worse metrics (ETa RMSE equal to 0.87 mm d-1) were probably caused by the presence of weeds due to the lack of maintenance provided by the farmer. SPARSE best performance was obtained using a-combination with LE RMSE equal to 53 W m-2. Noticeably, b- and c- combinations were implemented using a limited number of data (contextually to satellites acquisitions) thus achieving worse metrics (RMSE equal to 66 W m-2 and 93 W m-2 for b- and c- combinations, respectively).Satisfactory results gained permit this work to keep on being updated toward the synergies between the approaches for better ET estimation.

Marine plastic pollution has become a critical transboundary environmental issue, particularly affecting coastal regions with insufficient waste management infrastructure. This study applies a modified Lagrangian hydrodynamic model, TrackMPD v.1, to simulate the movement and accumulation of macroplastics in the West Africa Coastal Area. The research investigates three case studies: (1) the Liberia–Gulf of Guinea region, (2) the Mauritania–Gulf of Guinea coastal stretch, (3) the Cape Verde, Mauritania, and Senegal regions. Using both forward and backward simulations, macroplastics’ trajectories were tracked to identify key sources and accumulation hotspots. The findings highlight the cross-border nature of marine litter, with plastic debris transported far from its source due to ocean currents. The Gulf of Guinea emerges as a major accumulation zone, heavily impacted by plastic pollution originating from West African rivers. Interesting connections were found between velocities and directions of the plastic debris and some of the characteristics of the West African Monson climatic system (WAM) that dominates the area. Backward modelling reveals that macroplastics beached in Cape Verde largely originate from the Arguin Basin (Mauritania), an area influenced by fishing activities and offshore oil and gas operations. Results are visualized through point tracking, density, and beaching maps, providing insights into plastic distribution and accumulation patterns. The study underscores the need for regional cooperation and integrated monitoring approaches, including remote sensing and in situ surveys, to enhance mitigation strategies. Future work will explore 3D simulations, incorporating degradation processes, biofouling, and sinking dynamics to improve the representation of plastic behaviour in marine environments. This research is conducted within the Global Development Assistance (GDA) Agile Information Development (AID) Marine Environment and Blue Economy initiative, funded by the European Space Agency (ESA) in collaboration with the Asian. Development Bank and the World Bank. The outcomes provide actionable insights for policymakers, researchers, and environmental managers aiming to combat marine plastic pollution and safeguard marine biodiversity.

Plant species diversity is fundamental for the stability and resilience of ecosystems, and the well-being of the entire planet. Healthy and diverse ecosystems also contribute to air and water pollution removal, climate regulation and flood prevention. In the last century, plant biodiversity has been facing severe threats, such as habitat destruction and fragmentation due to increasing urbanization, deforestation, agricultural expansion, wildfires, and pollution. In addition, changes in climate pose significant threats to plants biodiversity conservation and native species preservation. All these natural and anthropic disturbance factors are profoundly modifying the competitive dynamics among plant species, often favouring the establishment and spread of some invasive plants, and exacerbating the biodiversity loss of native ecosystems. A well-known invasive alien species is Ailanthus altissima, a tree native to East Asia and introduced to various regions around the world, including North America and Europe. It is characterized by rapid growth, high reproductive capacity, and ability to thrive in a wide range of environmental conditions, where it can significantly modify ecosystems by altering soil characteristics, releasing allelopathic chemicals that may inhibit the growth of other plants, and forming dense thickets that reduce the space available and development chance of native vegetation. Ailanthus has been recognized as the most widespread and invasive alien tree species in Sicily (Italy), with a capillary presence over the entire regional territory, where it poses a serious threat to the biodiversity of the local Mediterranean ecosystems. Ecohydrological models can simulate vegetation dynamics and predict Ailanthus encroachment mechanisms also in presence of disturbance effects and under climate change. In this work, the CATGraSS, an ecohydrological Cellular Automata model (Zhou et al., 2013), has been used for simulating spatio-temporal dynamics of Ailanthus altissima in a specific site of “Vallone di Piano della Corte” Nature Reserve, in the Erei mountains in central Sicily (Italy). The study area has a surface of approximately 1 km2 and it is characterized by a relevant nucleus of Ailanthus that has been growing rapidly in recent years. The study aims to reconstruct Ailanthus altissima spatio-temporal evolution in the study area over the last century. The model has been calibrated using the current Ailanthus distribution maps, obtained by classifying high-quality satellite images, collected by PlanetScope constellation, exploiting modern remote sensing techniques, together with field surveys.

Accurate estimations of soil water content (SWC), transpiration (Ta) and actual evapotranspiration (ETa) are of utmost importance to evaluate crop water requirements and optimize water use efficiency. In this framework, the objective of this work was to assess the Soil Water Balance (SWB) model named SAMIR (SAtellite Montoring for Irrigation), considering two input combinations a) using in situ data exclusively and 2) using ERA-5 Land (ERA-5 Land, E5L) reanalysis meteorological variables. The main goal was to assess the suitability of E5L reanalysis data to be used as input to estimate SWC, Ta and ETa supposing that no in situ meteorological data are available. The experimental area is an 0.4 ha irrigated and fully equipped citrus orchard located in Sicily. The first analysis regarded the evaluation of errors associated with daily precipitation (P) and crop reference evapotranspiration (ETo) deduced from the E5L products. Thus, the comparison between measured and estimated daily SWC, Ta and ETa was carried out for seven irrigation seasons (2018–2024). Results highlighted that the Combination-a leaded to fairly good estimations of SWC, Ta and ETa, with values of root mean square error (RMSE) ranging between 0.01 and 0.02 cm3/cm3, 0.33 and 0.45 mm/d, 0.43 and 0.54 mm/d, respectively. As expected, less performances were achieved by Combination-b with RMSE in the range from 0.02 to 0.03 cm3/cm3 (SWC), from 0.38 to 0.42 mm/d (Ta), from 0.48 to 0.69 mm/d (ETa).

Marine litter is a globally recognized issue that impacts the environment and has significant negative effects on both human health and socio-economic activities. Mainly composed of plastic items, marine litter can be dispersed in the ocean by surface currents, sink to the seafloor, and/or be deposited on coastal areas. Monitoring, quantifying, and characterizing beach litter can be time-consuming but can be conducted using established monitoring protocols (both European and international) and supported by citizen science. Here, we present and discuss the main outcomes from in situ monitoring campaigns covering sandy beaches in both an urban area and a Marine Protected Area. Both macro- and meso-litter were quantified and identified by material, size, shape, and color. The quantity and heterogeneity of items (classified using the Joint List of Litter Categories for Macrolitter Monitoring) were highest in areas with the greatest user presence (e.g., refreshment areas, shops, and restaurants). Free-access beaches showed the highest density of macro-litter items compared to beaches where entrance was regulated by three levels of subscription. Artificial polymers/plastics, particularly plastic caps and lids, dominated, followed by paper and cardboard fragments. A database has been created allowing to highlight hotspots and patterns of occurrence that can inform local management measures, urging municipalities to improve waste management.

This paper describes a calibration procedure for a non-optimally configured High Frequency Radar (HFR) for the period 1 April 2021, to 31 March 2022, to assess sea waves characteristics. The HFR system, a 16.5 MHz WEllen RAdar (WERA), is part of an innovative network for monitoring the state of the sea. The system is installed in the western part of Sicily (Italy) where a wave buoy is positioned. HFR data underestimate the spectral significant wave heights (Hm0), in particular for Hm0 > 2 m, highlighting the need for calibration of the HFR system to ensure its optimal performance for operational purposes. The calibration was performed with both in-situ and modelled data provided by the Copernicus Marine Service. The best results were obtained when the buoy data were used as reference. Encouraging results were achieved as demonstrated by the improvement of the quantitative metrics after the calibration. Indeed, the RMSE decreased from 0.60 to 0.36 m; the correlation R increased slightly from 0.86 to 0.88, the slope from 0.48 to 0.8; whereas intercept from 0.11 to 0.31 m. Moreover, waves higher than > 2 m are well reproduced by the calibrated HFR time series with the RMSE decreasing from 1.3 to 0.53 m.

Marine plastic pollution is a global issue affecting ecosystems and various aspects of human life. The scientific community is exploring new monitoring and containment approaches. Because in-situ sampling campaigns are time and resource demanding, there is a focus on integrating different approaches for marine litter monitoring. Data of two in-situ surveys (using a manta net) were compared to sea surface currents data and derived products with the aim to find a proxy variable of the plastic occurrence. Sea surface currents data were provided by the CALYPSO HF network (operating in the Sicily Channel since 2012). Notably, the occurrence of fragment items is inversely correlated with the total kinetic energy (r2 ~ 0.85). This result was confirmed by a Lagrangian tracking model considering the deployment of virtual drifters around each in-situ measurement point. The proposed method applied to a wider domain using Copernicus Marine Service (CMS) data revealed that high plastic accumulation areas could be located at the centre of eddies often occurring in the winter period. However, uncertainties arise by the moderate-low correlation found between HF CALYPSO and CMS sea current data.

Marine pollution is a growing global issue, impacting both marine ecosystem and human health. High quantities of debris, mainly composed by plastic items, have been identified both in the coastal area and in the sea environment. Remote sensing techniques represent an useful tool (complementary to the in-situ campaigns) to monitor litter in the coastal environment, especially if the spectral signatures of the debris are known. In this framework, harvested beach litter (plastic items especially) were collected from two sandy beaches. The samples were spectrally characterised by implementing two indoor laboratory experiments with the aim to infer the best wavelengths to be used for beach litter detection via the spectral angle mapper index. Due to lack of a scientific protocol concerning the spectral data acquisition, two experimental setups were carried out to simulate the direct and diffuse illumination conditions. For around 30% of the samples, the spectral signatures are influenced by the two experimental setups. Outcomes suggest that for the majority of the samples green, blue, red-edge and some infrared bands are suitable for the beach litter detection.

Marine pollution is a growing global issue, impacting both marine ecosystem and human health. High quantities of debris, mainly composed by plastic items, have been identified both in the coastal area and in the sea environment. The difficulties to perform intensive in situ monitoring campaigns, especially in remote areas, could be overcome by the application of remote sensing techniques which can be effectively driven if the spectral signatures of the marine debris are known. In this framework, harvested marine debris (plastic items especially) were collected from two sandy beaches and were spectrally characterized by implementing two indoor laboratory experiments. Due to lack of a scientific protocol concerning the spectral data acquisition, two illumination conditions were tested. The data acquired, employing the two setups, were compared and the spectral angle mapper index (evaluated among each sample and sand) allowed evaluating the influences of the illumination conditions on the possible outdoor detection using remote sensors. Outcomes evidenced that for around 20% of the samples, the setup played a role in the detection of the harvested debris. For the majority of the samples green, blue, red-edge and some infrared bands are suitable for the detection with the WorldView-3 resulting the more promising nowadays operating satellite platform.

In this work, we study a Mediterranean cyclone, Helios, which took place during 9–11 February 2023 in the southeastern part of Sicily and Malta, by a multiparametric approach combining microseism results with sea state and meteorological data provided by wavemeter buoy, HF radar, hindcast maps and satellite SEVIRI images. The sub-tropical system Helios caused heavy rainfall, strong wind gusts and violent storm surges with significant wave heights greater than 5 m. We deal with the relationships between such a system and the features of microseism (the most continuous and ubiquitous seismic signal on Earth) in terms of spectral content, space–time variation of the amplitude and source locations tracked by means of two methods (amplitude-based grid search and array techniques). By comparing the location of the microseism sources and the area affected by significant storm surges derived from sea state data, we note that the microseism location results are in agreement with the real position of the storm surges. In addition, we are able to obtain the seismic signature of Helios using a method that exploits the coherence of continuous seismic noise. Hence, we show how an innovative monitoring system of the Mediterranean cyclones can be designed by integrating microseism information with other techniques routinely used to study meteorological phenomena.

The amount of plastics on seawater is causing ecosystem damage to both land areas and water bodies. It is proven that once plastic particles reach the sea, they will be degraded. As their detection is not easy with in situ sampling, remote sensing techniques could help detect and evaluate their impact on ecosystems. To understand the main limitations of detecting floating plastic material through remote images, an a pilot experiment was carried out on an artificial floating plastic target deployed at the Aegean Sea (Greece) within the Plastic Litter Project 2021. Hyperspectral PRISMA, multispectral PlanetScope and visible Unmanned Aerial Systems (UAS) acquisitions were analysed. A nonlinear unmixing technique was applied to derive the spectral signature of the plastic target; finally, the linear unmixing approach allowed for determining the plastic percentage occupation at the pixel level. Different band combinations of the PRISMA data were selected to evaluate which provided the best result for the detection; one of these band combinations was retrieved via the principal component analysis. Only slight differences were achieved using the different PRISMA band-sets. As expected, since the artificial plastic target had a diameter comparable with the PRISMA spatial resolution (similar to 30 m), its detection was a challenging task caused by the water influence in the pixel (mixed pixels). The detection was realized by benefitting from the high amount of available spectral bands, as confirmed by the comparative test with a PlanetScope image used at its original spatial resolution (3 m) and after degrading it at the PRISMA level. Results demonstrated that an optimal target detection was possible with few spectral bands, only taking advantage of the high spatial resolution (compared with the target dimension). Indeed, unreliable plastic fractions were derived at the PRISMA spatial resolution with a limited number of spectral bands.

Accurate estimations of crop water requirements are necessary to improve water use in agriculture and to optimize the use of available freshwater resources. To this aim, Agro-Hydrological models allow to quantify crop water requirements which depend on actual crop evapotranspiration (ET a ). The use of remotely sensed data is a way to accurately estimate time series of ET a 2D maps. Indeed, the use of remote observations represents a reliable strategy to identify the spatial distribution of vegetation biophysical parameters, such as crop coefficients (K c and/or K cb ) under actual field conditions. The objective of this research was to assess the crop water requirements and irrigation scheduling inside an irrigation district, located near to Castelvetrano, Sicily (Italy), characterized mainly by olives orchards, using the FAO56 Agro-Hydrological model joint with a functional relationship between basal crop coefficient (K cb ), and the Normalized Difference Vegetation Index (NDVI) obtained from Sentinel-2 Multispectral Images (MSI) - level 2A. FAO56 Agro-Hydrological model was applied for the 2018 irrigation season. The model was implemented in two different modes to estimate spatial and temporal variability of the ET a , soil water content (SWC) in the root zone, as well as the irrigation scheduling. In the Castelvetrano irrigation district 1/A, a linear K cb (NDVI) relationship was identified following the Allen and Pereira (A&P) procedure which is based on the knowledge of the canopy characteristics, meteorological variables, and the fraction of vegetation cover (f c ); the latter estimated via the NDVI. The difference between irrigation volumes provided by the farmer and estimated by the model was equal to 3%. This encouraging result highlights that the proposed model can be a useful tool for supporting the decision in the irrigation demands management in the district.