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Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at 0. 1 ∘ × 0. 1 ∘ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation ( r ≥ 0.7 ) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures. [ABSTRACT FROM AUTHOR]

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. [ABSTRACT FROM AUTHOR]

Non‐perennial streams have a global prevalence, but quantitative knowledge of the temporal dynamics of their flowing length—namely the extent of the wet portion of the stream network—remains limited, as the monitoring of the spatiotemporal configuration of wet channels is challenging in most settings. This work combines the high spatial resolution of visual surveys and the high temporal resolution of camera‐based approaches to reconstruct the space‐time stream network dynamics in a 3.7 km2 Mediterranean catchment of central Italy. Information on the hydrological status of the stream network derived from 40 field surveys and sub‐hourly images collected with 21 stage‐cameras are combined exploiting the hierarchical principle. The latter postulates the existence of a Bayesian chain, defined from the local persistence of the nodes that dictates their wetting/drying order during expansion/retraction cycles of the flowing stream network. Our results highlight the complexity of network dynamics in the study area: while the number of wet nodes decreases during the dry season and increases during the wet season, the local persistency exhibits a highly heterogeneous non‐monotonic spatial pattern, originating a dynamically disconnected network. Despite this heterogeneity, the hierarchical model well approximates the temporal evolution of the state of the network nodes, with an accuracy that exceeds 99%. Crucially, the model allows the reconstruction of the wet portion even in cases in which part of the network was not observed. This work provides a novel conceptual approach for the reconstruction of the wet portion of the network in poorly accessible sites. Key Points: Field data on the hydrological status of the drainage network are collected via heterogeneous spatiotemporal resolution techniquesData are combined to reconstruct the long‐term active drainage network dynamics exploiting the hierarchical principleThe model reproduces the complexity of the non‐monotonic pattern of the wet portion of the network in the study site [ABSTRACT FROM AUTHOR]

Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dual-polarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase (Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differential-phase data are immune to attenuation and less affected by hail contamination. In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R(Zh,Kdp) data and to compare the results with those derived using R(Zh) and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radar-based rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series. [ABSTRACT FROM AUTHOR]

The Mediterranean basin is traditionally a hotspot where copious amounts of water vapor at low- and mid-tropospheric levels often favor atmospheric instability and the deepening of storms, leading to intense rainfall events with consequent flash floods. Moreover, this region includes sharp land–sea transitions, narrow maritime areas, and mountain chains which enhance convective precipitation. In this study, radar precipitation data were used to investigate the spatial distribution of rainfall swaths for seven severe cyclones originating over the Mediterranean Sea which produced intense flash inundation events along the western coast of Italy in the last decade (2011–2020). Based on 5 min precipitation amounts gridded at a 1 km spatial resolution, the temporal evolution of these storms displays a curvilinear path moving from sea to inland. Results show that more than half of the total precipitation for the analyzed events occurred on sea, and the total amount of storm rainfall over the marine surface exceeded that over land in four events out of the seven. Since the coastline strongly affects the rainfall pattern, we analyzed the land–sea discontinuity, which is a key factor controlling the spatial distribution of storm rates through their trajectory, where a small shift in precipitation target might smooth ground effects and mitigate flood impacts. [ABSTRACT FROM AUTHOR]

The modeling and simulation of topography-induced imaging distortions are crucial for consistent radiometric information exploitation in current and forthcoming SAR-based Earth observation missions with a high spatial and temporal resolution, with relevance in several applications. In this paper, for the first time, we specifically investigate the compensation of topography-induced radiometric distortions affecting SAR images acquired by the L-band Argentinian satellite SAOCOM. We adopt a recently developed calibration method relying on an analytical formulation derived in the rigorous framework of the differential geometry of surfaces. We first provide an original interpretation of the analytical formulation, thus providing further insights into the relevant area-stretching-based formalism. Then, the numerical implementation of the method is specialized to systematically process the data acquired by SAOCOM sensors; hence, the resulting sensor-specific prototype solution processor is employed in this study. Finally, experiments performed over a real scenario in the southern part of Italy, characterized by large topography variations, are presented and discussed, thus elucidating the effectiveness of the adopted method applied to SAOCOM images. The adopted effective SAR calibration strategy opens up the way to its operational use in large-scale SAOCOM data processing. [ABSTRACT FROM AUTHOR]

The implementation of the energy transition and the building of energy communities are driving forward the exploitation of the potential for rooftop photovoltaic power generation. Estimating rooftop PV generation potential requires the processing of different types of data, such as the cadastral information of buildings, a detailed description of available rooftop areas, and solar irradiance data. High-resolution estimation based on GIS data is normally limited to small survey areas. Instead, by using an algorithm for the efficient calculation of shadows over rooftops, and the integration of solar irradiance over time, we developed a procedure that allows for the rapid full census assessment of rooftop photovoltaic potential with a spatial resolution of 1 m, applicable at the regional scale and requiring minimal computational resources. We applied this approach to the rooftops of buildings in Sardinia, an island and region of Italy of particular interest for the energy transition. In addition to estimating the geographic potential, we carried out a preliminary assessment of the technical and economic potential, yielding a maximal photovoltaic rooftop generation potential of 22 TWh for the entire region. [ABSTRACT FROM AUTHOR]

The effectiveness of domestic rainwater harvesting (DRWH) systems to support urban flood resilience is analysed at the sub-catchment scale, according to a specific DRWH conversion scenario, under 4 degrees of urbanization, 3 drainage network configurations, 4 precipitation regimes and 3 return periods of the rainfall event. At this aim, a suitable modelling framework is implemented: the semi-distributed hydrologic-hydraulic model is undertaken using EPASWMM 5.1.007 where specific tools are developed to simulate DRWH systems at high spatial resolution. The effectiveness of the DRWH systems simulated for the 144 different cases, is analysed at the event scale by using the Volume and Peak Reduction indexes to measure the hydrologic performance. The dimensionless variable, namely the event storage fraction, is defined in order to easily describe the DRWH effectiveness. The event storage fraction is defined as the ratio between the event runoff volume resulting from the impervious surface of the urban catchment in the reference scenario and the storage capacity of the DRWH systems. Modelling results confirm that DRWH catchment-scale applications allow to support specific stormwater control requirements based on peak-flow or volume regulations strategies. Findings of the elaboration reveal for a typical residential catchment in the Italy-France cross-border coastal area, that DRWH effectiveness in supporting the urban flood management becomes significant (i.e. Volume and Peak Reduction indexes greater than 0.2) starting from a storage event fraction of 0.4 that means realizing storage tanks able to contain at least the 40% of runoff volume generated by the targeted event at the sub-catchment scale. [ABSTRACT FROM AUTHOR]

A comprehensive and reliable water balance of snow‐dominated alpine catchments is required for a holistic analysis of the hydrological and hydrogeological processes. A major limitation to the elaboration of this balance in alpine terrain is the difficulty of data acquisition as well as the limited presence of meteorological stations. Remotely sensed data can provide valuable information for the water balance assessment on a regional scale. We exploited Sentinel‐satellite data to estimate the groundwater storage for one hydrologic year in an extensive Alpine catchment located in northern Italy by means of the residual water balance approach. In particular, Evapotranspiration (ET) and Snow Water Equivalent were estimated with the combined use of Sentinel data, at a spatial resolution of 20 and 30 m, respectively. The results show that the adopted satellite‐based methods allow obtaining consistent and physically realistic values to describe the groundwater storage dynamics. In the period 2018–2020, a positive storage occurred only during the snowmelt period and the overall storage was negative, leading to a net lowering of the groundwater level in the floodplain. In addition, the influence of physiographic parameters (altitude, slope, and aspect) and seasonality on the estimates of ET and snow‐depth were investigated. Key Points: A new methodology to quantify groundwater storage dynamics in snow‐dominated catchments by the residual water balance approach is presentedHydrological components are estimated with a high temporal and spatial resolution by a novel approach based on synergistic use of Sentinel dataResults contribute to the understanding of hydrological processes in Alpine areas and the expected effects of climate change [ABSTRACT FROM AUTHOR]

Climatology of extreme rainfalls for a certain location is commonly taken into account designing stormwater management systems. Rain gauge data have often been used to estimate rainfall intensity for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable on low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can both underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. This study circumvents these shortcomings by using specific differential phase (Kdp) data from dualpolarization C-band weather radars. The rain intensity estimates based on specific differential phase are immune to attenuation and affected less by hail contamination. This study aims to estimate depth-duration-frequency (DDF) curves computed using polarimetric weather radar data using quantitative precipitation estimations (QPEs) based on Kdp data and to compare the results with the DDF curves derived using rain-gauge data. Only the warm period of the year is here considered, as most of the extreme precipitation events take place at this time. Limiting the dataset to warm period also allows us to use the radar-based rainfall quantitative precipitation estimates, which are more reliable than the snowfall ones. Single C-band polarimetric weather radar site data are used both from Italy and Estonia. This study demonstrates that polarimetric weather radar observations can provide a reliable QPEs compared to rain gauges and, that even relatively short time series can provide a reliable estimation of the rainfall return periods in climatological homogeneous areas. [ABSTRACT FROM AUTHOR]

The performance of a large number of critical infrastructure systems needs to be periodically re-evaluated. This is especially so when such systems are located in seismic areas and are subjected to ageing effects. Seismic re-evaluations are typically performed using numerical response history analyses based on a geotechnical model of the infrastructure and using hazard-consistent ground motions. We depart from the Viadotto Italia (the tallest multi-span bridge in Italy, located in a high-seismicity region) to draw best practices on how to construct a robust geotechnical model and derive appropriate target response spectra to be used in forward applications. Our proposed framework starts with the analysis of historical and new information and data. We then describe how to perform a multi-epoch consistency analysis that deals with the reliability and level of uncertainty of the data, culminating with the definition of a pragmatic geotechnical model that builds upon all available data, including investigation information produced at different spatial resolutions and quality levels. We also propose a consistent approach to perform site-specific probabilistic seismic hazard analysis to develop appropriate ground motions. This last step builds upon experiences with a data-rich high-seismicity zone in southern Italy, where both shallow crustal faults and deep subduction sources are present. [ABSTRACT FROM AUTHOR]

The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions. To understand and quantify the impacts of increasing logging activities and climatic changes in the European Alps, accurate information on the occurrence and distribution of mountain pine stands is needed. While Earth observation provides up-to-date information on land cover, space-borne mapping of mountain pines is challenging as different coniferous species are spectrally similar, and small-structured patches may remain undetected due to the sensor's spatial resolution. This study uses multi-temporal optical imagery from PlanetScope (3 m) and Sentinel-2 (10 m) and combines them with additional features (e.g., textural statistics (homogeneity, contrast, entropy, spatial mean and spatial variance) from gray level co-occurrence matrix (GLCM), topographic features (elevation, slope and aspect) and canopy height information) to overcome the present challenges in mapping mountain pine stands. Specifically, we assessed the influence of spatial resolution and feature space composition including the GLCM window size for textural features. The study site is covering the Sarntal Alps, Italy, a region known for large stands of mountain pine. Our results show that mountain pines can be accurately mapped (PlanetScope (90.96%) and Sentinel-2 (90.65%)) by combining all features. In general, Sentinel-2 can achieve comparable results to PlanetScope independent of the feature set composition, despite the lower spatial resolution. In particular, the inclusion of textural features improved the accuracy by +8% (PlanetScope) and +3% (Sentinel-2), whereas accuracy improvements of topographic features and canopy height were low. The derived map of mountain pines in the Sarntal Alps supports local forest management to monitor and assess recent and ongoing anthropogenic and climatic changes at the treeline. Furthermore, our study highlights the importance of freely available Sentinel-2 data and image-derived textural features to accurately map mountain pines in Alpine environments. [ABSTRACT FROM AUTHOR]

We analyse the expected characteristics of drought events in northern Italy for baseline (1971–2000), near (2021–2050), and far (2071–2100) future conditions, estimating the drought spatial extent and duration, the percentage of affected area, and the frequency of drought episodes. To this end, daily ensembles of precipitation and temperature records from Global Climate Models (GCMs) and Regional Climate Models (RCMs) pairs, extracted from EURO-CORDEX and MED-CORDEX for the RCP 4.5 and 8.5 scenarios, are collected at spatial resolution of 0.11 degrees. Before the analysis, model outputs are validated on daily weather station time series, and scaling factors for possible use in bias correction are identified. Annual temperature and precipitation anomalies for near and far future conditions are investigated; drought events are identified by the standardized precipitation evapotranspiration index and standardized precipitation index at the 12-, 24-, and 36-month timescales. This study highlights the importance of using multiple drought indicators in the detection of drought events, since the comparison reveals that evapotranspiration anomaly is the main triggering factor. For both scenarios, the results indicate an intensification of droughts in northern Italy for the period 2071–2100, with the Alpine chain being especially affected by an increase of drought severity. A North-to-South spatial gradient of drought duration is also observed. [ABSTRACT FROM AUTHOR]

• Accurate pollen forecasts can help the self-management of allergic diseases. • We have developed pollen dispersion models for a region in northern Italy, 2019. • High-resolution input data on vegetation cover improved tree pollen prediction. • These improvements were more pronounced in mountainous area. Accurate pollen forecasting models can help the self-management of allergic respiratory diseases. Our study introduces and validates, for the first time, a pollen modelling system covering the Veneto Region (Italy) at the 3 km spatial resolution for 2019. The model simulated the pollen dispersion, diffusion and deposition processes, using vegetation cover (VC) maps, phenological pollen emission algorithms, and meteorological forecasting. We have specifically analysed the influence of the spatial resolution of VC maps on predicted airborne pollen concentrations for alder, birch, olive, grass, and ragweed. Two VC datasets were used: CAMS VC: the European CAMS dataset at ca. 10 km horizontal resolution; detailed VC: high-resolution datasets (from 250 m to 1 km spatial resolution). Predicted daily averaged concentrations obtained with CAMS and detailed VC were compared to the observations collected at 15 monitoring stations using model performance indicators and pollen seasonal-derived parameters. A stratified analysis assessed performance variations in lowland versus mountain environments. The results showed a reduction of the root mean square error (RMSE) for alder and birch pollen using the detailed VC (detailed VC vs. CAMS VC: 15.7 vs. 133.6; 17.8 vs. 52.5 p/m3, respectively), while higher RMSE resulted for grass (24.5 vs. 20.7 p/m3). Similar RMSEs were obtained for olive and ragweed pollen (3.8 vs. 4.0; 3.9 vs. 3.9 p/m3, respectively). Results from the differences in Seasonal Pollen Integrals (SPIn) were consistent with the RMSE patterns. The onset of pollen seasons was more accurately predicted than their end. The general improvement of pollen predictions obtained with the detailed VC was particularly evident in the mountains. Incorporating data from detailed vegetation maps into atmospheric dispersion models has significantly improved predictions for arboreal pollen (alder, birch, olive), especially in complex surfaces where high-resolution input data is crucial. [Display omitted] [ABSTRACT FROM AUTHOR]

Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowdsourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of "landslide events". This source of information allows continuous feedback from real world, and news concerning landslide events can be rapidly collected. In this work, the newspaper articles about landslides in Italy are automatically collected by an existing data mining algorithm, based on a semantic engine. The news has been analysed to assess their distribution over the territory and to verify the possibility of using them for hazard mapping purpose. In 10 years, from 2010 to 2019, the algorithm identified and geolocated 184322 articles referring to 32525 generical events ("news"). At first, the collected data underwent to a manual verification, followed by a classification based on news relevance, localization accuracy and time of publication. Then, these data have been used to identify the areas and the periods most affected by landslide phenomena. The analyses show that almost 42% of Italian municipalities have been affected by landslide. According to the results, the use of data mining is helpful for the creation of landslide databases where the day and the approximative location (municipality) of the possible landslide triggers are known. This database, in turn, can be used for scientific purposes, as the definition of the meteorological condition associated with landslide initiation, the validation of risk maps. It can also be used for a proper land use or risk mitigation planning, since the most landslide-prone municipalities can be defined. [ABSTRACT FROM AUTHOR]

In this work, the results of a sensitivity study based on ICON-LAM simulations at 2.5 km of spatial resolution over the Italian area, driven by ECMWF IFS global data, are presented. The main aim is to provide a contribution to the selection of suitable parameterization schemes that result in more effective for a proper representation of the Italian climate features. Model evaluation was conducted in terms of the air temperature and precipitation for three subregions, comparing a set of 13 simulations against SCIA and E-OBS standard datasets. In addition, evaluation was also conducted against selected data stations scattered over the Italian area. We found that the ICON-LAM model was able to provide a good representation of the temperature over Italy, whereas non-negligible biases were observed for precipitation in certain regions. The model proved to be sensitive to changes in physical parameterization schemes. In particular, we found that the explicit treatment of deep convection and the "clouds as in turbulence" scheme for cloud cover allowed for a better representation of precipitation in the summer over the Alpine region. The single moment scheme is currently the best option for cloud microphysics. [ABSTRACT FROM AUTHOR]

The combined effect of topography and near-surface heterogeneities on the seismic response is hardly predictable and may lead to an aggravation of the ground motion. We apply physics-based numerical simulations of 3D seismic wave propagation to highlight these effects in the case study of Arquata del Tronto, a municipality in the Apennines that includes a historical village on a hill and a hamlet on the flat terrain of an alluvial basin. The two hamlets suffered different damage during the 2016 seismic sequence in Central Italy. We analyze the linear visco-elastic seismic response for vertically incident plane waves in terms of spectral amplification, polarization and induced torsional motion within the frequency band 1–8 Hz over a 1 km2 square area, with spatial resolution 25 m. To discern the effects of topography from those of the sub-surface structure we iterate the numerical simulations for three different versions of the sub-surface model: one homogeneous, one with a surficial weathering layer and a soil basin and one with a complex internal setting. The numerical results confirm the correlation between topographic curvature and amplification and support a correlation between the induced torsional motion and the topographic slope. On the other hand we find that polarization does not necessarily imply ground motion amplification. In the frequency band above 4 Hz the topography-related effects are mainly aggravated by the presence of the weathering layer, even though they do not exceed the soil-related effects in the flat-topography basin. The geological setting below the weathering layer plays a recognizable role in the topography-related site response only for frequencies below 4 Hz. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) represent a fundamental resource in geomorphological analysis. The increasing availability of open‐access DEMs over wide areas is advantageous, but requires an evaluation of DEM quality and errors. This work applies a hierarchical assessment of global, continental and national DEMs in Italy in order to explore the differences and analyze the vertical accuracy, spatial error distribution and agreement of morphometric measurements. The selected DEMs are compared with local reference data as a ground points dataset, extracted from the national geodetic network, and regional DEMs at high spatial resolution, through both a qualitative and a quantitative approach. The results identify limits and potentialities of the selected DEMs, showing accuracy and errors in height representation, also affected by the topographic characteristics of the surface, such as steep slope in mountain zones and some defects in hydromorphological derivatives that could condition the geomorphological applications. [ABSTRACT FROM AUTHOR]

Space-time (ST) multiexponential cell (MultiEXCELL), a ST correlated rainfall model oriented to the analysis of radio-propagation impairments (1 km × 1 km spatial resolution and 1-min temporal resolution), is presented. The model is developed on the basis of a comprehensive rain field database collected by the Spino d'Adda, Italy, weather radar. Rain cells are modeled using an exponentially shaped profile, whose main parameters evolve over time according to simple yet effective analytical expressions. The spatial correlation of the rain rate across the field is preserved by reproducing the natural aggregative process of single rain cells into larger clusters, while the temporal evolution of the rain field is achieved by combining the structural change of the rain cells and their displacement across the field, at the same time taking the realistic evolution of the fractional rainy area into account. When tested against local data, the time series generated by ST MultiEXCELL show to correctly reproduce the spatial and temporal correlation of the rain rate. These encouraging results suggest the use of ST MultiEXCELL as a comprehensive tool for the design and performance assessment of Earth-space and terrestrial millimeter-wave systems implementing advanced mitigation techniques relying on the uneven distribution of the rain rate in space and time. [ABSTRACT FROM AUTHOR]

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while in situ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions (r2 = 0.59 for gravimetric method; r2 = 0.65 for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis. [ABSTRACT FROM AUTHOR]

• A modelling strategy is applied to estimate net forest carbon fluxes in tuscany. • Forest NPP and NEE are predicted at high spatial resolution (23 m). • NPP is assessed versus ground measurements of GSV current annual increment. • NEE is validated versus integrated eddy covariance flux tower observations. This paper presents the application of a recently proposed modelling strategy to yield high spatial resolution estimates of net forest carbon fluxes in Tuscany (Central Italy). The simulation of forest net primary production (NPP) and net ecosystem production (NEP) is based on the combination of remotely sensed and ancillary data which describe the main characteristics of local environment and vegetation. Distinctively, the methodology is driven by a map of growing stock volume (GSV) having a pixel size of 23 × 23 m2 and can therefore yield correspondingly high spatial resolution estimates of forest NPP and NEP. An advancement of the original methodology is also proposed based on the availability of a recently produced soil organic carbon map of Tuscany informative about biomass decomposition (heterotrophic respiration). The modified modelling strategy is applied over the period 2001–2005 and the obtained estimates are assessed against: i) ground observations of GSV current annual increment (CAI) collected for over 600 plots during the last National Forest Inventory of Italy; ii) a high spatial resolution reference NEP map obtained for a Mediterranean pine forest (San Rossore) by the integration of eddy covariance flux data and local CAI observations. Considering the complexity of the simulated processes and of the examined environment, the estimation accuracy achieved is satisfactory for both NPP and NEP. This supports the possibility of applying the proposed modelling strategy to estimate net carbon fluxes at high spatial resolution in other forest environments. [ABSTRACT FROM AUTHOR]

Sulfur dioxide (SO2) degassing at Strombolian volcanoes is directly associated with magmatic activity, thus its monitoring can inform about the style and intensity of eruptions. The Stromboli volcano in southern Italy is used as a test case to demonstrate that the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (Sentinel-5P) satellite has the suitable spatial resolution and sensitivity to carry out local-scale SO2 monitoring of relatively small-size, nearly point-wise volcanic sources, and distinguish periods of different activity intensity. The entire dataset consisting of TROPOMI Level 2 SO2 geophysical products from UV sensor data collected over Stromboli from 6 May 2018 to 31 May 2021 is processed with purposely adapted Python scripts. A methodological workflow is developed to encompass the extraction of total SO2 Vertical Column Density (VCD) at given coordinates (including conditional VCD for three different hypothetical peaks at 0–1, 7 and 15 km), as well as filtering by quality in compliance with the Sentinel-5P Validation Team's recommendations. The comparison of total SO2 VCD time series for the main crater and across different averaging windows (3 × 3, 5 × 5 and 4 × 2) proves the correctness of the adopted spatial sampling criterion, and practical recommendations are proposed for further implementation in similar volcanic environments. An approach for detecting SO2 VCD peaks at the volcano is trialed, and the detections are compared with the level of SO2 flux measured at ground-based instrumentation. SO2 time series analysis is complemented with information provided by contextual Sentinel-2 multispectral (in the visible, near and short-wave infrared) and Suomi NPP VIIRS observations. The aim is to correctly interpret SO2 total VCD peaks when they either (i) coincide with medium to very high SO2 emissions as measured in situ and known from volcanological observatory bulletins, or (ii) occur outside periods of significant emissions despite signs of activity visible in Sentinel-2 data. Finally, SO2 VCD peaks in the time series are further investigated through daily time lapses during the paroxysms in July–August 2019, major explosions in August 2020 and a more recent period of activity in May 2021. Hourly wind records from ECMWF Reanalysis v5 (ERA5) data are used to identify local wind direction and SO2 plume drift during the time lapses. The proposed analysis approach is successful in showing the SO2 degassing associated with these events, and warning whenever the SO2 VCD at Stromboli may be overestimated due to clustering with the plume of the Mount Etna volcano. [ABSTRACT FROM AUTHOR]

Purpose: The Australian Reduced Range Extended Spatial Test (ARREST) approach was designed to improve visual field spatial resolution while maintaining a similar test duration to clinically used testing algorithms. ARREST does not completely threshold visual field locations with sensitivity < 17 dB, and uses the presentations saved to test new locations in areas of steep gradient within the visual field. Previous assessments of ARREST's performance have used computer simulation. In this study, we cross-sectionally assessed the performance of ARREST in people with visual field loss., Methods: We tested 23 people with glaucoma (mean age: 71 ± 8 years) with established visual field loss. Three visual field procedures were performed using the Open Perimetry Interface: cZEST and ARREST on the Octopus 900 perimeter (Haag-Streit AG, Switzerland), and a reference standard (best available estimate [BAE]) on the Compass perimeter (CenterVue SpA, Italy). ARREST was compared against the cZEST and the BAE., Results: On average, ARREST added seven new locations (range = 0-15) to a visual field test. There was no significant difference in the number of stimulus presentations between procedures (mean = 259 ± 25 [ARREST] vs. 261 ± 25 [cZEST], P = 0.78). In classifying threshold values < 17 dB, ARREST performed similarly when compared against BAE., Conclusions: This study provides empirical evidence to support conclusions from previous computer simulations that ARREST can be used to increase spatial sampling in regions of interest without increasing test time., Translational Relevance: ARREST is a new approach that augments current visual field testing procedures to provide better spatial description of visual field defects without increasing test duration., Competing Interests: Disclosure: V. Muthusamy, None; A. Turpin, Haag-Streit AG (R), Heidelberg Engineering GmBH (R), and CenterVue SpA (R, C); M.J. Walland, None; B.N. Nguyen, None; A.M. McKendrick, Haag-Streit AG (R), Heidelberg Engineering GmBH (R), and CenterVue SpA (R, C), (Copyright 2020 The Authors.)