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Accurate estimations of actual crop evapotranspiration are of utmost importance to evaluate crop water requirements and to optimize water use efficiency. At this aim, coupling simple agro-hydrological models, such as the well-known FAO-56 model, with remote observations of the land surface could represent an easy-to-use tool to identify biophysical parameters of vegetation, such as the crop coefficient Kc under the actual field conditions and to estimate actual crop evapotranspiration. This paper intends, therefore, to propose an operational procedure to evaluate the spatio-temporal variability of Kc in a citrus orchard characterized by the sporadic presence of ground weeds, based on micro-meteorological measurements collected on-ground and vegetation indices (VIs) retrieved by the Sentinel-2 sensors. A non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as the normalized difference vegetation index, NDVI, and the normalized difference water index, NDWI, is suitable to represent the spatio-temporal dynamics of the investigated environment, characterized by sparse vegetation and the sporadic presence of spontaneous but transpiring soil weeds, typical of winter seasons and/or periods following events wetting the soil surface. The Kc values obtained in each cell of the Sentinel-2 grid (10 m) were then used as input of the spatially distributed FAO-56 model to estimate the variability of actual evapotranspiration (ETa) and the other terms of water balance. The performance of the proposed procedure was finally evaluated by comparing the estimated average soil water content and actual crop evapotranspiration with the corresponding ones measured on-ground. The application of the FAO-56 model indicated that the estimated ETa were characterized by root-mean-square-error, RMSE, and mean bias-error, MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated soil water contents, SWC, were characterized by RMSE equal to 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of soil water content and actual crop evapotranspiration under the investigated field conditions.

The opportunity of using wave energy converter arrays in a hot spot area of the Mediterranean sea was investigated. For this purpose, numerical simulations of wave energy converters together with a simple cost-effective analysis were carried out. The selected study area is one of the most energetic inside the Sicilian channel (Italy). The numerical simulations were performed using the SNL-SWAN (“Sandia National Laboratories – Simulating WAves Nearshore”) spectral model with an unstructured mesh. The power absorbed from thirteen Wave Energy Converter (WEC) devices was computed. In particular, Pelamis, Wave Dragon, Oyster 2, Wavestar, B-HBA, B-OF, Bref-HB, Bref-SHB, F–2HB, F–3OF, F-HBA and F-OWC were investigated. Hydrodynamic stationary simulations of a WEC array parallel to the coastline were carried out in order to choose the best devices for a non-stationary simulation. The devices selected for this simulation are Pelamis, Wave Dragon, Oyster 2, F–2HB, and Bref-HB. The non-stationary simulation was forced with thirty-nine years wave parameters reanalysis from ERA5 by ECMWF (European Centre for Medium-range Weather Forecasts). This simulation has allowed to obtain the capacity factor (CF) and the dissipative effects in the period from 1979 to 2018. Moreover, a preliminary cost-to-benefit analysis was carried out for the best performance WEC array only. Costs, revenues, LCoE (Levelized Cost of Energy), and payback period were therefore computed by means of economic assumptions. The high energy concentration near the coast produces hydrodynamic effects which have as a consequence potential erosion phenomena. A shoreline change analysis of three beaches of the studied coast was carried out. Such an analysis was focused on shoreline retreatment during the last thirty years. On the basis of the presented results, there is a slight economic advantage in installing a Wave Dragon scaled array with the added value of potentially giving beneficial effects on coastal erosion. Nevertheless, from a strictly economic point of view, other renewable resources could give better results (e.g. wind energy). The adopted methodology gives encouraging results that confirm the opportunity to deploy WEC devices in the Sicilian Channel together with devices exploiting other renewable resources.

Monitoring the state of the sea is a fundamental task for economic activities in the coastal zone, such as transport, tourism and infrastructure design. In recent years, regular wave height monitoring for marine risk assessment and mitigation has become unavoidable as global warming impacts in more intense and frequent swells.In particular, the Mediterranean Sea has been considered as one of the most responsive regions to global warming, which may promote the intensification of hazardous natural phenomena as strong winds, heavy precipitation and high sea waves. Because of the high density population along the Mediterranean coastlines, heavy swells could have major socio-economic consequences. To reduce the impacts of such scenarios, the development of more advanced monitoring systems of the sea state becomes necessary.In the last decade, it has been demonstrated how seismometers can be used to measure sea conditions by exploiting the characteristics of a part of the seismic signal called microseism. Microseism is the continuous seismic signal recorded in the frequency band of 0.05 and 0.4 Hz that is likely generated by interactions of sea waves together and with seafloor or shorelines.In this work, in the framework of i-WaveNET INTERREG project, we performed a regression analysis to develop a model capable of predicting the sea state in the Sicily Channel (Italy) using microseism, acquired by onshore instruments installed in Sicily and Malta. Considering the complexity of the relationship between spatial sea wave height data and seismic data measured at individual stations, we used supervised machine learning (ML) techniques to develop the prediction model. As input data we used the hourly Root Mean Squared (RMS) amplitude of the seismic signal recorded by 14 broadband stations, along the three components, and in different frequency bands, during 2018 - 2021. These stations, belonging to the permanent seismic networks managed by the National Institute of Geophysics and Volcanology INGV and the Department of Geosciences of the University of Malta, consist of three-component broadband seismometers that record at a sampling frequency of 100 Hz.As for the target, the significant sea wave height data from Copernicus Marine Environment Monitoring Service (CMEMS) for the same period were used. Such data is the hindcast product of the Mediterranean Sea Waves forecasting system, with hourly temporal resolution and 1/24° spatial resolution. After a feature selection step, we compared three different kinds of ML algorithms for regression: K-Nearest-Neighbors (KNN), Random Forest (RF) and Light Gradient Boosting (LGB). The hyperparameters were tuned by using a grid-search algorithm, and the best models were selected by cross-validation. Different metrics, such as MAE, R2 and RMSE, were considered to evaluate the generalization capabilities of the models and special attention was paid to evaluate the predictive ability of the models for extreme wave height values.Results show model predictive capabilities good enough to develop a sea monitoring system to complement the systems currently in use.

Technological advances over last decades gave an innovative impulse to the development of new streamflow measurements techniques, especially regarding remote flow monitoring methods that allow for non-intrusive measurements. The most widely used image-based techniques are the optical ones, such as Large-Scale Particle Image Velocimetry (LS-PIV) and Large-Scale Particle Tracking Velocimetry (LS-PTV). The dynamic movement of tracers floating on the water surface of a river is recorded, and the resulting videos are processed by applying a statistical cross-correlation analysis to detect the most probable frame-by-frame displacement, finally deriving the surface velocity field. To obtain river discharge, it is necessary to couple the geometry of a river cross-section with the assessed surface velocity field, adopting simplified assumptions on the vertical velocity profiles. The accuracy of these techniques depends on several factors, such as the processing models set-up, the tracer density and distribution, the video-length, and many other aspects related to environmental and hydraulic conditions. The aim of this work is the identification of an automatic methodology aimed to select an optimal video sub-sequence over the entire recorded video in order to optimize the performance of software based on LS-PIV technique. For this purpose, the results of multiple field campaigns on two Sicilian rivers (Italy), i.e., Oreto and Platani rivers, are exploited. Results are shown in terms of error in the estimation of the surface velocity profile along a specific transect, comparing velocity values assessed by the LS-PIV technique and by an Acoustic Doppler Current Profiler (ADCP).

Technological advances over last decades gave an innovative impulse to development of new streamflow measurements techniques, making possible to implement remote flow monitoring methods that allow for non-intrusive measurements. Here, we focus on image-based techniques that involve the use of digital camera, either installed on a bridge or equipped by a drone (UAVs – Unmanned Aerial Vehicles). The most widely known and used optical techniques are the Large-Scale Particle Image Velocimetry and the Large-Scale Particle Tracking Velocimetry. Optical techniques are based on four main steps: (i) seeding and recording, (ii) images pre-processing, (iii) images processing, and (iv) images post-processing. Tracer, naturally present on the water surface or artificially introduced, is assumed to move jointly with the surface liquid particles. Tracer dynamic is recorded and the resulting videos are processed by specific software, applying a statistical cross-correlation analysis to detect the most probable frame-by-frame tracer displacements. To obtain river discharge, it is then necessary to combine the geometry of the river cross-section with the assessed surface velocity field, often adopting simplified assumptions about the vertical velocity profiles.The accuracy of these techniques depends on several factors, such as the size of the interrogation area, the seeding density, the video length, and many other aspects related to environmental and hydraulic conditions, that are less investigated in the scientific literature. The aim of this work is to exploit the results of an extensive field measurement campaign on several Sicilian rivers (Italy) to infer useful insights for the parametric setting of the two most popular open-source processing LS-PIV software (i.e, PIVlab and FUDAA-LSPIV). The field campaign includes discharge measures carried out at different sites, taking into account different roughness conditions and cross-sections, and, for each site, in different seasons, accounting then for different environmental and hydraulic conditions. Topographical surveys were preliminary performed on each site to obtain detailed DEMs, which are used in the pre-processing phase for image stabilization and orthorectification. Video sequences were acquired from both bridge and drone, using wood chips as tracer. Benchmark measures were also retrieved by ADCP.

Tsunamis are among the most dangerous natural disasters for coastal areas experiencing tsunami hazard. One of the major concerns in the assessment of strategies for the risk mitigation is to estimate vulnerability of structures and infrastructures. However, reliable approaches for the evaluation of the structural capacity under tsunami loads are nowadays not always available for all the types of structures, especially masonry. On this aim, the paper deals with the modeling issues of 3D masonry structures subjected to tsunami loads and the effect on the structural behavior of different modeling approaches. First, a brief state of the art on the available studies is presented regarding the evaluation of tsunami forces on buildings and the studies carried out on the determination of structural capacity. Then, an advanced structural analysis software based on the Finite Element Method (FEM) is used to analyze the influence of parameters such as load type, masonry type and interaction properties type. Finally, an application on a typical two-storey masonry structure subjected to tsunami load according to the actual codes is presented and discussed, providing recommendations on the assumptions to be made for the definition of a reliable numerical model. The findings of the numerical simulations revealed that the structural response is strongly influenced by the modeling approach defined for the masonry walls in terms of interaction properties and the type of connection between orthogonal walls. Moreover, it was observed that, as for the case of seismic analysis, when local behavior governs the failure, lower structural capacity is obtained, while in the case of global behavior governing the failure, higher structural performances are provided.

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. Quantification of the spatial and temporal behaviour of SM at field scale is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Uncrewed Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, UAS data can help the construction of downscaling models which can link the land surface features and SM to identify the importance level of each predictor. To optimize the usage of data from UAS surveys for generating high-resolution SM at field scale, a comparative study of various SM retrieval or downscaling methods can be beneficial.In this study, four methods, which include the apparent thermal inertia method, Kubelka–Munk method (KM), simplified temperature-vegetation triangle method, and random forest model (RF), were compared by theory background, data requirements, operation procedures and SM estimation results. The above-mentioned models have been tested using UAS data and point measurements collected on the Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). A number of long-term studies on the vadose zone have been conducted across a range of spatial scales. The thermal inertia model is built upon the dependence of the thermal diffusion on SM, which were inferred from diachronic thermal infrared data. The Kubelka–Munk Model is a spectral model to retrieve surface SM using optical data. The simplified temperature–vegetation triangle model, was used to map surface SM based on simultaneous information of the vegetation coverage and surface temperature. In addition, we also introduce an SM downscaling method using the RF model and SENTINEL-1 CSAR 1km SM product.The study is concluded with the inter-comparison of methods. The results from KM have the highest resolution which is the same as the input multispectral data. The results of RF and KM provides information only for bare soil pixels according to the principle of the model. Results show good performances for all methods, but the simplified triangle and thermal inertia model provides better performances in terms of correlation coefficient and RMSE measured with respect to in-situ measurements. In addition, it is worthy to say that the RF downscaling method reveals the features controlling the spatial distributions of SM at a different scale.This research is a part of EU COST-Action “HARMONIOUS” and waterJPI project “iAqueduct”.

To study on a regional basis, the relation between fluvial sediment delivery and coastal erosion, the historical record of coastline migration of Sicily was analyzed with respect to the estimated sediment delivery to the coast obtained from the spatially distributed sediment delivery WaTEM/SEDEM model. The latter was directly acquired from the ESDAC database as a 25 m pixel layers, being based on the combination between the RUSLE model and a transport capacity routing algorithm.At the same time, the coastline-evolution (accretion/retreatment) data for 1960/1994 and 1994/2012 intervals were processed. This dataset, provided by ISPRA (Italian Institute for Environmental Protection and Research), is made by vectorial polygons, corresponding to erosion or accretion areas obtained by the intersection between two coastlines. The dataset contains polygons related to the 1960-1994 and 1994-2012 periods.Once a common baseline was extracted from 2019 satellite images, 22 Physiographic Units (PU) were identified. The PU was defined based on geomorphologic criteria and by assuming a null net sediment budget (null sediment transport between two PU neighboring). Each coastal PU was connected to its contributing fluvial basins, also assigning the expected sediment delivery at the coastline.To perform the analysis, cross profiles along the coastline were generated and intersected with the polygons, calculating a response value, in terms of retreatment or accretion, to each of the cross-profile centroids. Finally, for each PU, the cumulated variations were computed.PUs with significant cumulative variations (more than 2 km) in at least one of the two epochs were identified and three different patterns were detected: accretion/retreatment, retreatment/accretion, and retreatment/retreatment. The response observed for the different PUs was then analyzed considering estimated sediment delivery, recognizing coherent (large sediment delivery = accretion) and incoherent (large sediment delivery = retreatment) behaviors, which have been interpreted as controlled by the history of soil/coastal erosion management practices.In particular, in spite of a very high expected sediment delivery, more than three-quarters of the Tyrrhenian coast resulted as affected by a marked retreat in 60-94 (same tens of meters) and a moderate accretion in 94-12, as the result of extensive coastal works which have been realized to mitigate coastal erosion.

The Mediterranean Sea is a prominent climate-change hot spot, with many socioeconomically vital coastal areas being the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, high-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in coastal ocean observing systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-user and science-driven requirements, addressing regional challenges in three main topics: (i) maritime safety, (ii) extreme hazards and (iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean coastal areas required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to provide a set of recommendations for future improvement prospects to maximize the contribution to extending science-based HFR products into societally relevant downstream services to support blue growth in the Mediterranean coastal areas, helping to meet the UN's Decade of Ocean Science for Sustainable Development and the EU's Green Deal goals., peer-reviewed

Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with high-frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near-real time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavors between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to do the following: (i) harmonize deployment and maintenance practices; (ii) standardize data, metadata, and quality control procedures; (iii) centralize data management, visualization, and access platforms; and (iv) develop practical applications of societal benefit that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events, the investigation of transport processes, and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socioeconomic, technical, and scientific challenges to be faced during the implementation of this integrated HFR regional network., This study has been partially developed in the framework of the Interreg MED Strategic Project SHAREMED, co-financed by the European Regional Development Fund under the funding program Interreg MED 2014–2020. Website: https://sharemed.interreg-med.eu/ (last access: 31 March 2022). We are also grateful for the partial support of the (i) the project PO FEAMP 2014/2020 (Misura 2.51) funded by Regione Campania (Italy), (ii) the 2017 PRIN project EMME (Exploring the fate of Mediterranean microplastic: from distribution pathways to biological effects) funded by the Italian Ministry for Research (grant agreement no. 2017WERYZP), and (iii) the CMEMS-INSTAC phase II, which provides the context of the activities for HFR data harmonization, standardization, and distribution. Collaborative discussion on data management harmonization at the European level has also been carried out thanks to the contribution of the projects INCREASE (CMEMS Service Evolution Call for Tenders 21-SE-CALL1) and SeaDataCloud (EU-H2020 GA no. 730960).

Shoreline evolution studies are fundamental to assess the rate of beach accretion or retreatment. In the last years, researchers developed many methods with several techniques belonging to different disciplines and backgrounds. Nowadays, the Shoreline Change Analysis (SCA) is the most widespread method to assess shoreline evolution. In fact, the SCA is needed to perform management strategies able to respond to current erosion or potential future erosion problems. This paper presents a new approach using an automatic method for SCA with accuracy and speed improvements. Indeed, in the framework of the management of coastal erosion and to develop an adaptation plan to coastal hazards it is necessary to study the links existing between coastal and marine morphological transformations. A new tool was written in Matlab, the input files were built using a GIS environment and the results were arranged in a GIS project. The present tool adopts two approaches: the Transect Based Analysis (TBA) and the Area Based Analysis (ABA). New roughness and irregularity indexes were defined to calculate the transects’ spacing. Moreover, a piecewise polynomial baseline was defined to improve accuracy in shoreline position assessment. The approaches TBA and ABA were validated both by synthetic benchmarks and by a study case. The adopted synthetic benchmarks represent very common types of beaches: with one or more salients, with cusps and groynes. The case study was a logarithmic spiral Mediterranean beach on the southwestern coast of Sicily. All the studied test cases provided good results in terms of accuracy and easiness of use. The new tool and methodology, with TBA and ABA coupling, encloses many practical aspects. Indeed, ABA allows the general assessment of advancing/retreating beach areas, whereas TBA is needed to study very short stretches of beach. The proposed SCA analysis is useful for the requirements of a complete coastal management plan and to evaluate erosion management options. Moreover, the SCA gives a sound technical understanding of the physical coastal processes and should engage the community, the stakeholders and the agencies responsible for development application decisions. The new approach gives high-quality results achieved with significant speediness making it suitable for regional studies and directly providing parameters needed for erosion managing strategies

The Mediterranean Sea is a prominent climate change hot spot, being their socio-economically vital coastal areas the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, High-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for a continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in the Coastal Ocean Observing Systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-users and science-driven requirements, addressing regional challenges in three main topics: i) maritime safety; ii) extreme hazards; iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean region required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to finally provide a set of recommendations for the future improvement prospects to maximize the contribution in extending the science-based HFR products into societal relevant downstream services to support the blue growth in the Mediterranean coastal areas, helping to meet the UN’s Decade of Ocean Science for Sustainable Development and the EU’s Green Deal goals.

Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with High-Frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near real-time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavours between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to: i) harmonize deployment and maintenance practices; ii) standardize data, metadata and quality control procedures; iii) centralize data management, visualization and access platforms; iv) develop practical applications of societal benefit, that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events or the investigation of transport processes and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socio-economic, technical and scientific challenges to be faced during the implementation of this integrated HFR regional network.

The development of new image-based techniques is allowing a radical change in the environmental monitoring field. The fundamental characteristics of these methods are related to the possibility of obtaining non-intrusive measurements even in adverse circumstances, such as high flow conditions, which may seriously threaten the operators’ safety conditions in traditional approaches.Optical techniques, based on the acquisition, analysis and elaboration of sequences of images acquired by digital cameras, are aimed at a complete characterization of the river instantaneous surface velocity field, through the analysis of a floating tracer, which may be naturally present or artificially introduced. The growing availability of a new generation of both low-cost optical sensors and high-performing free software programs for image processing, is a key aspect explaining the rapid development of such techniques in recent years. The best known optical techniques are the large scale particle velocimetry (LSPIV) and the large scale particle tracking velocimetry (LSPTV).This work is aimed to analyze and compare the performance of the two most common free software packages based on LSPIV (i.e. the PIVlab and the FUDAA-LSPIV), which use different cross-correlation algorithms. The test is carried out by analyzing several sequences of both synthetic images and real frames acquired on natural rivers under different environmental conditions (with tracers artificially introduced). An image sequences generator has been implemented ad-hoc with the aim to create, under fixed configurations, synthetic sequences of images, simulating uniformly distributed tracers moving under controlled conditions. The various configurations are characterized by different parameterization in terms of: (i) flow velocity (S=slow or F=fast flow conditions, according to a logarithmic transverse flow profile); (ii) tracer particles size (CON= disks of constant diameter; VAR=disks of variable size with given mean diameter); (iii) seeding density per frame (density: low -LD, medium -MD, high -HD).The synthetic sequences are processed by the two software packages together with the real sequences, analyzing the errors in terms of mean value of the surface velocity field and velocity along a transverse transect, with respect to a benchmark velocity (i.e. that imposed in the image sequence generator for the synthetic sequences and that deriving from the use of traditional sensors, i.e. ADCP, for the real sequences).

In the original article [...]

A network of High-Frequency radar (HFR) stations runs operationally in the Malta-Sicily Channel (MSC), Central Mediterranean Sea, providing sea surface current maps with high temporal (1 h) and spatial (3 × 3 km) resolutions since August 2012. Comparisons with surface drifter data and near-surface Acoustic Doppler Current Profiler (ADCP) observations, as well as radar site-to-site baseline analyses, provide quantitative assessments of HFR velocities accuracy. Twenty-two drifters were deployed within the HFR domain of coverage between December 2012 and October 2013. Additionally, six ADCP vertical current profiles were collected at selected positions during a dedicated field survey. External radio-frequency interferences lead to significant gaps in the HFR coverage, which were minimized by an interpolation technique applied to the surface current fields. The validation of HFR velocities was first performed for each individual station in the network, using data generated with both the ideal and the measured antenna patterns. The validation was then repeated with pairs of HFR stations using the site-to-site baseline error analysis. The analysis confirms that the baseline error estimations and the full validation metrics for this HFR frequency band are consistent, and in some aspects superior, when compared to results from similar studies in other regions. The validation with drifter tracks suggests that the adopted interpolation procedure does not in general impact on the comparison metrics, but may introduce biases when used to extrapolate currents in regions where radar geometrical constraints are suboptimal. The analysis of the percentage of contribution of individual HFR stations shown that the quality of HFR data is influenced by the network geometry.

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

In recent years, technological advances have been observed in environmental monitoring field, leading to a rapid spread of innovative technologies overcoming many historical challenges. In river monitoring field the use of image-based techniques provides non-intrusive measurements ensuring the best safety conditions for operators. The most used optical methods are the Large-Scale Particle Image Velocimetry (LSPIV) and the Large-Scale Particle Tracking Velocimetry (LSPTV).In LSPIV and LSPTV techniques a floating tracer is introduced on the water surface and its motion is recorded by commercial devices (e.g. digital cameras). Resulting videos are then processed by free and open source software which applies a statistical cross-correlation analysis to provide the instantaneous surface velocity field.The aim of this work is to investigate the performance of the most widely used LSPIV software in estimating the surface velocity field taking into account the presence of turbulent structures. Indeed a typical feature of natural river is the presence of turbulent eddies which makes the tracer patterns above the water surface difficult to predict. The evaluation of tracer particle displacement is further complicated by the negative phenomenon of aggregation; it influences cross-correlation causing an incorrect estimation of the velocity vectors.The study of the hydraulic turbulence of a natural river has been tackled from a numerical point of view. PANORMUS (Parallel Numerical Open-Source Model for Unsteady Flow Simulations) package (Napoli, 2011) has been used by adopting a LES (Large Eddy Simulation) scheme. PANORMUS is a numerical tool coded to solve the 3D momentum equations for incompressible flows (Navier-Stokes and Reynolds equations) using the Finite-Volume Method (FVM). The analyses were carried out on real cases modelled with PANORMUS-LES package. The hydraulic reconstructed domains are characterised by regular cross sections, accurately derived from real topographic survey campaigns, and low river-bed roughness (smooth concrete surface).Synthetic sequences of tracer motion were derived from the hydraulic model and then processed by using LSPIV software.The results of such numerical analyses have allowed an evaluation of LSPIV performance assessing the errors in terms of mean value of the surface velocity field and velocity along transverse transects.

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.Keywords: soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS

Remote Sensing (RS) information has progressively found, in recent years, more and more applications in hydrological modelling as a valuable tool for easy and frequent collection of geophysical data. However, this kind of data should be handled carefully, minding its characteristics, spatial resolution and the heterogeneity of the target area.In this work, a scale analysis on evapotranspiration estimates over heterogeneous crops is performed combining a distributed energy-water balance model (FEST-EWB) and high-resolution remotely-sensed Land Surface Temperature (LST) and vegetation data.The FEST-EWB model is calibrated on measured LST, based on a procedure where every single pixel is modified independently one from the other; hence in each pixel of the analysed domain the minimum of the pixel difference between modelled RET and satellite observed LST is searched over the period of calibration.The case study is a Sicilian vineyard, with test dates in the summer of 2008. Meteorological and energy fluxes data are available from an eddy-covariance station, while LST and vegetation data are obtained from low-altitude flights at the high resolution of 1.7 metres.After a preliminary calibration on LST data and validation on energy fluxes, the scale analysis is performed in two ways: model input aggregation and model output aggregation. Four coarser scales are selected in reference to some common satellite products resolution: 10.2 m (in reference to Sentinel’s 10 m), 30.6 m (Landsat, 30 m), 244.8 m (MODIS visible, 250 m) and 734.4 m (MODIS, 1000 m). First, modelled surface temperature and evapotranspiration are aggregated to each scale by progressive averaging. Then, model inputs are upscaled to the same spatial resolutions and the model is calibrated anew, obtaining independent results directly at the target scale.The results of the two procedures are found to be quite similar, testifying to the capacity of the model to provide accurate products for a heterogeneous area even at low resolutions. The robustness of the analysis is strengthened by a further comparison with two well-established energy-balance algorithms: the one source Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) model.