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Robust Satellite Technique (RST) was applied to detect small-scale landslides along electrical lines in Sicily, Italy. To this end, electrical poles were selected as targets within the study area. The methodology, implemented in Google Earth Engine (GEE) environment, exploits the Copernicus Sentinel-2 platform to identify anomalous land cover variation, in terms of Normalized Difference Vegetation Index (NDVI), possibly related to small displacements affecting electric poles. Since the applied methodology is based on land cover change, dense vegetation plays an important role in detecting small-scale landslides. Therefore, we targeted months with the highest vegetation density, such as February, March, and April from 2016–2023. The results obtained reveal that out of the five targeted electrical poles, four of them exhibited anomalies > 2-sigma indicating significant changes in land cover possibly related to local ground movement as confirmed by aerial photos collected in the period 2015–2023. Our findings reveal anomalies of −2.17 and −2.36 on 7/17/2017 and 9/05/2017 for pole 1. For pole 2, the results show an anomaly of −2.02 on 8/11/2018. The results also indicate anomalies of −4.40 and −2.99 on 7/09/2021 and 9/27/2022 for pole 3. For pole 4, the findings show an anomaly of −3.10 on 1/18/2019.

Abstract The China Seismo-Electromagnetic Satellite (CSES), with a sun-synchronous orbit at 507 km altitude, was launched on 2 February 2018 to investigate pre-earthquake ionospheric anomalies (PEIAs) and ionospheric space weather. The CSES probes manifest longitudinal features of four-peak plasma density and three plasma depletions in the equatorial/low-latitudes as well as mid-latitude troughs. CSES plasma and the total electron content (TEC) of the global ionosphere map (GIM) are used to study PEIAs associated with a destructive M7.0 earthquake and its followed M6.5 and M6.3/M6.9 earthquakes in Lombok, Indonesia, on 5, 17, and 19 August 2018, respectively, as well as to examine ionospheric disturbances induced by an intense storm with the Dst index of − 175 nT on 26 August 2018. Anomalous increases (decreases) in the GIM TEC and CSES plasma density (temperature) frequently appear specifically over the epicenter days 1–5 before the M7.0 earthquake and followed earthquakes, when the geomagnetic conditions of these PEIA periods are relatively quiet, Dst: − 37 to 19 nT. In contrast, TEC and CSES plasma parameter anomalies occur globally in the southern hemisphere during the storm days of 26–28 August 2018. The CSES ion velocity shows that the electric fields of PEIAs associated with the M7.0 earthquake are 0.21/0.06 mV/m eastward and 0.11/0.10 mV/m downward at post-midnight/post-noon on 1–3 August 2018, while the penetration electric fields during the storm periods of 26–28 August 2018 are 0.17/0.45 mV/m westward/downward at post-midnight of 02:00 LT and 0.26/0.26 mV/m eastward/upward at post-noon of 14:00 LT. Spatial analyses on CSES plasma discriminate PEIAs from global effects and locate the epicenter of possible forthcoming large earthquakes. CSES ion velocities are useful to derive PEIA- and storm-related electric fields in the ionosphere.

This study proposes a method for determining the optimal period for crop yield prediction using Sentinel-2 Vegetation Index (VI) measurements. The method operates at the single-field scale to minimize the influence of external factors, such as soil type, topography, microclimate variations, and agricultural practices, which can significantly affect yield predictions. By analyzing historical VI data, the method identifies the best time window for yield prediction for specific crops and fields. It allows adjustments for different space–time intervals, crop types, cloud probability thresholds, and variable time composites. As a practical example, this method is applied to a wheat field in the Po River Valley, Italy, using NDVI data to illustrate how the approach can be implemented. Although applied in this specific context, the method is exportable and can be adapted to various agricultural settings. A key feature of the approach is its ability to classify variable-length periods, leveraging historical Sentinel-2 VI compositions to identify the optimal window for yield prediction. If applied in regions with frequent cloud cover, the method can also identify the most effective cloud probability threshold for improving prediction accuracy. This approach provides a tool for enhancing yield forecasting over fragmented agricultural landscapes.

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time.

In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables.

In this work, a solar illumination model of the Earth’s atmosphere is developed. The developed model allows us to determine with extreme accuracy how the atmospheric illumination varies during night hours on a global scale. This time-dependent variation in illumination causes a series of sudden changes in the entire Earth-atmosphere-ionosphere system of considerable interest for various research sectors and applications related to climate change, ionospheric disturbances, navigation and global positioning systems. The use of the proposed solar illumination model to calculate the time-dependent Solar Terminator Height (STH) at the global scale is also presented.Time-dependent STH impact on the measurements of ionospheric Total Electron Content (TEC) is, for the first time, investigated on the basis of 20 years long time series of GPS-based measurements collected at ground. The correlation analysis, performed in the post-sunset hours, allows new insights into the dependence of TEC–STH relation on the different periods (seasons) of observation and solar activity conditions.

Over the last decade, thanks to the availability of historical satellite observations that have begun to be significantly large and thanks to the exponential growth of artificial intelligence techniques, many advances have been made in the detection of geophysical parameters such as seismic-related anomalies. In this study, the variations of the ionospheric Total Electron Content (TEC), one of the main parameters historically proposed as a seismic-connected indicator, are analyzed. To make a statistically robust analysis of the complex phenomena involved, we propose a completely innovative machine-learning approach developed in the R programming language. Through this approach, an optimal setting of the multitude of methodological inputs currently proposed for the detection of ionospheric anomalies is performed. The setting is optimized by analyzing, for the first time, multi-year—mostly twenty-year—time series of TEC satellite data measured by global navigation satellite systems (GNSS) over the Italian region, matched with the corresponding multi-year time series of seismic events. Seismic events including all the countries of the Mediterranean area, up to Turkey, are involved in the analysis. Tens of thousands of possible combinations of input methodological parameters are simulated and classified according to pre-established criteria. Several inputs examined return clear results. These results combined with each other highlight the presence of anomalous seismic-related sequences that have an extremely low probability of having been detected randomly (up to 2 out of 1 million). The anomalies identified represent the most anomalous behaviors of the TEC recorded during the entire period under investigation (e.g., 20 years). Some of the main conclusions are that, at mid-latitudes, ① the detection of seismic-TEC anomalies can be more efficient looking for punctual rather than persistent phenomena; ② the optimal thresholds for the identification of co-seismic anomalies can assume different values depending on type of anomaly (positive or negative) and type of observation; ③ single GNSS receiver data can be useful for capturing local earthquake-ionospheric effects and Global Ionospheric Maps (GIM) data can be functional in detecting large-scale earthquake-ionospheric effects; ④ earthquakes deeper than 50 km are less likely to affect the ionosphere.

Fire radiative power (FRP) is a basic parameter for fire characterization since it represents the heat emission rate of fires. Moreover, its temporal integration (fire radiative energy, FRE) is used as a proxy for estimating biomass burning and emissions. From satellite, FRP is generally computed by comparing the Medium InfraRed (MIR) signal of the fire pixel with the background value on the event image. Such an approach is possibly affected by some issues due to fire extent, clouds and smoke over the event area. The enlargement of the background window is the commonly used gimmick to face these issues. However, it may include unrepresentative signals of the fire pixel because of very different land use/cover. In this paper, the alternative Background Radiance Estimator by a Multi-temporal Approach (BREMA), based on the Robust Satellite Technique (RST), is proposed to characterize background and compute FRP. The approach is presented using data from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) platform. Moreover, BREMA is here combined with the RST-FIRES (RST for FIRES detection) technique for fire pixel identification and the σ-SEVIRI retrieval algorithm for transmittance evaluation. Results compared to the operational SEVIRI-based FRP-PIXEL product, although highly correlated in terms of background radiance (r2 = 0.95) and FRP values (r2 = 0.96), demonstrated a major capability of BREMA to estimate background radiances regardless of cloudiness or smoke presence during the event and independently on fire extent. The possible impact of the proposed approach on the estimates of CO2 emissions was also evaluated for comparison with the Global Fire Emissions Database (GFED4s).

Landslides are catastrophic natural phenomena occurring as a consequence of climatic, tectonic, and human activities, sometimes combined among them. Mostly due to climate change effects, the frequency of occurrence of these events has quickly grown in recent years, with a consequent increase in related damage, both in terms of loss of human life and effects on the involved infrastructures. Therefore, implementing properly actions to mitigate consequences from slope instability is fundamental to reduce their impact on society. Satellite systems, thanks to the advantages offered by their global view and sampling repetition capability, have proven to be valid tools to be used for these activities in addition to traditional techniques based on in situ measurements. In this work, we propose an advanced multitemporal technique aimed at identifying and mapping landslides using satellite-derived land cover information. Data acquired by the Multispectral Instrument (MSI) sensor aboard the Copernicus Sentinel-2 platforms were used to investigate a landslide affecting Pomarico city (southern Italy) in January 2019. Results achieved indicate the capability of the proposed methodology in identifying, with a good trade-off between reliability and sensitivity, the area affected by the landslide not just immediately after the event, but also a few months later. The technique was implemented within the Google Earth Engine Platform, so that it is completely automatic and could be applied everywhere. Therefore, its potential for supporting mitigation activities of landslide risks is evident.

Many islands of the Aegean Sea show strong geothermal activity due to volcanism in the area. In this paper, Robust Satellite Techniques (RST) are used to isolate, from other known possible sources, those thermal anomalies possibly related to geothermal activity in the Miocene volcanic field of Lesvos Island (Northern Aegean). For this purpose, 12 years (2003–2014) of daily Night-time Land Surface Temperature (LST) products, from Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions were analyzed. The final dataset contained 770 thermal anomalies whose spatial correlation with geological and structural data of Lesvos - such as caldera rims, ring faults, major tectonic lineaments and hydrothermal alterations mapped by processing SENTINEL-2 MSI satellite images – has been particularly investigated. In the approximately 20 ma geothermal field of Lesvos, geothermal activity seems to be also associated with the extensional regime of the broader area that leads to lithosphere thinning and consequent heat transfer in the multi-fractured terrain of Lesvos through volcanic and tectonic faults. Achieved results seem to confirm the possibility to use RST-based thermal anomalies to identify temporal variations in the geothermal activity probably due to the uplifting and circulation of the hydrothermal waters.

In the original article [...]

Climate-induced drought events are responsible for forest decline and mortality in different areas of the world. Forest response to drought stress periods may be different, in time and space, depending on vegetation type and local factors. Stress analysis may be carried out by using field methods, but the use of remote sensing may be needed to highlight the effects of climate-change-induced phenomena at a larger spatial and temporal scale. In this context, satellite-based analyses are presented in this work to evaluate the drought effects during the 2000s and the possible climatological forcing over oak forests in Southern Italy. To this aim, two approaches based on the well-known Normalized Difference Vegetation Index (NDVI) were used: one based on NDVI values, averaged over selected decaying and non-decaying forests; another based on the Robust Satellite Techniques (RST). The analysis of the first approach mainly gave us overall information about 1984–2011 rising NDVI trends, despite a general decrease around the 2000s. The second, more refined approach was able to highlight a different drought stress impact over decaying and non-decaying forests. The combined use of the RST-based approach, Landsat satellite data, and Google Earth Engine (GEE) platform allowed us to identify in space domain and monitor over time significant oak forest changes and climate-driven effects (e.g., in 2001) from the local to the Basilicata region scale. By this way, the decaying status of the Gorgoglione forest was highlighted two years before the first visual field evidence (e.g., dryness of apical branches, bark detachment, root rot disease). The RST exportability to different satellite sensors and vegetation types, the availability of suitable satellite data, and the potential of GEE suggest the possibility of long-term monitoring of forest health, from the local to the global scale, to provide useful information to different end-user classes.

Central Apennines are one of the highest seismic risk regions in Italy. A number of energetic events (MW > 5) struck the region during the period 2004–2017, killing several hundreds of people (e.g., 294 casualties associated with the August 24th, 2016, MW 6.0 event of Amatrice). These earthquakes impacted piezometric levels, springs discharges, and groundwater chemistry across a large area, even at distances of dozens of kilometers from the epicenters. Here we present a multidisciplinary dataset based on hydrogeochemical and satellite observations associated with the seismic events that occurred in Central Italy during the period 2004–2017, which combines information derived from the application of groundwater monitoring and satellite techniques. Groundwater monitoring techniques allowed for the detection of hydrogeochemical anomalies in spring and well waters (14 water sampling points in total, with 22 variations larger than 2σ), while satellite techniques were applied to detect time-space variations in ground thermal emissions. We detected two significant, almost synchronous, anomalies in 2009 and 2016–2017 with both techniques, and we tentatively correlated them to crustal deformation processes. Part of the observed signals were detected before mainshocks, and they appear to be related to aseismic slip or to seismic slip eventually induced by minor fluctuations in seismicity. We argue that the combination of two factors, i.e., the shallow depth of local earthquakes and the concurrent deepening of groundwater circulation paths to several km depth, allow for the recording of variations in the stress field by geofluids released at the surface.

The paper provides, for the first time, a long-term (>10 years) analysis of anomalous transients in Earth’s emitted radiation over Turkey and neighbouring regions. The RST (Robust Satellite Techniques) approach is used to identify Significant Sequences of Thermal Anomalies (SSTAs) over about 12 years (May 2004 to October 2015) of night-time MSG-SEVIRI satellite images. The correlation analysis is performed with earthquakes with M ≥ 4, which occurred in the investigated period/region within a pre-defined space-time volume around SSTA occurrences. It confirms, also for Turkey, the possibility to qualify SSTAs among the candidate parameters of a multi-parametric system for time-Dependent Assessment of Seismic Hazard (t-DASH). After analysing about 4000 images (about 400 million of single satellite records), just 155 SSTAs (about 4 every 100 images) were isolated; 115 (74% out of the total) resulted in earthquake-related (false-positive rate 26%). Results of the error diagram confirms a non-casual correlation between RST-based SSTAs and earthquake occurrences, with probability gain values up to 2.2 in comparison with the random guess. The analysis, separately performed on Turkish areas characterized by different faults and earthquakes densities, demonstrates the SSTA correlation with a dynamic seismicity more than with static tectonic settings.

Investigating the variability of phytoplankton phenology plays a key role in regions characterized by cyclonic circulation regimes or convective events, like the north-western Mediterranean Sea (NWM). The main goal of this study is to assess the potential of the robust satellite techniques (RST) in identifying anomalous phytoplankton blooms in the NWM by using 9 years (2008–2017) of multi-sensor chlorophyll-a (chl-a) products from the CMEMS and OC-CCI datasets. Further application of the RST approach on a corresponding time-series of in situ chl-a measurements acquired at the BOUSSOLE site allows evaluation ofthe accuracy of the satellite-based change detection indices and selecting the best indicator. The OC-CCI derived chl-a anomaly index shows the best performances when compared to in situ data (R2 and RMSE of 0.75 and 0.48, respectively). Thus, it has been used to characterize an anomalous chl-a bloom that occurred in March 2012 at regional scale. Results show positive chl-a anomalies between the BOUSSOLE site and the Center of Convection Zone (CCZ) as a possible consequence of an intense convection episode that occurred in February 2012.

On 16 February 2021, an eruptive paroxysm took place at Mt. Etna (Sicily, Italy), after continuous Strombolian activity recorded at summit craters, which intensified in December 2020. This was the first of 17 short, but violent, eruptive events occurring during February–April 2021, mostly at a time interval of about 2–3 days between each other. The paroxysms produced lava fountains (up to 1000 m high), huge tephra columns (up to 10–11 km above sea level), lava and pyroclastic flows, expanding 2–4 km towards East and South. The last event, which was characterised by about 3 days of almost continuous eruptive activity (30 March–1 April), generated the most lasting lava fountain (8–9 h). During some paroxysms, volcanic ash led to the temporary closure of the Vincenzo Bellini Catania International Airport. Heavy ash falls then affected the areas surrounding the volcano, in some cases reaching zones located hundreds of kilometres away from the eruptive vent. In this study, we investigate the Mt. Etna paroxysms mentioned above through a multi-platform satellite system. Results retrieved from Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), starting from outputs of the Robust Satellite Techniques for Volcanoes (RSTVOLC), indicate that the 17th paroxysm (31 March–1 April) was the most intense in terms of radiative power, with values estimated around 14 GW. Moreover, by the analysis of SEVIRI data, we found that the 5th and 17th paroxysms were the most energetic. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), providing shortwave infrared (SWIR) data at 20/30 m spatial resolution, enabled an accurate localisation of active vents and the mapping of the areas inundated by lava flows. In addition, according to the Normalized Hotspot Indices (NHI) tool, the 2nd (17–18 February) and 7th (28 February) paroxysm generated the largest thermal anomaly at Mt. Etna after April 2013, when Landsat-8 OLI data became available. Despite the impact of clouds/plumes, pixel saturation, and other factors (e.g., satellite viewing geometry) on thermal anomaly identification, the used multi-sensor approach allowed us to retrieve quantitative information about the 17 paroxysms occurring at Mt. Etna. This approach could support scientists in better interpreting changes in thermal activity, which could lead to future and more dangerous eruptions.

The RST (Robust Satellite Techniques) approach is a multi-temporal scheme of satellite data analysis widely used to investigate and monitor thermal volcanic activity from space through high temporal resolution data from sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In this work, we present the results of the preliminary RST algorithm implementation to thermal infrared (TIR) data, at 90 m spatial resolution, from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results achieved under the Google Earth Engine (GEE) environment, by analyzing 20 years of satellite observations over three active volcanoes (i.e., Etna, Shishaldin and Shinmoedake) located in different geographic areas, show that the RST-based system, hereafter named RASTer, detected a higher (around 25% more) number of thermal anomalies than the well-established ASTER Volcano Archive (AVA). Despite the availability of a less populated dataset than other sensors, the RST implementation on ASTER data guarantees an efficient identification and mapping of volcanic thermal features even of a low intensity level. To improve the temporal continuity of the active volcanoes monitoring, the possibility of exploiting RASTer is here addressed, in the perspective of an operational multi-satellite observing system. The latter could include mid-high spatial resolution satellite data (e.g., Sentinel-2/MSI, Landsat-8/OLI), as well as those at higher-temporal (lower-spatial) resolution (e.g., EOS/MODIS, Suomi-NPP/VIIRS, Sentinel-3/SLSTR), for which RASTer could provide useful algorithm’s validation and training dataset.

The total suspended matter (TSM) variability plays a crucial role in a lake’s ecological functioning and its biogeochemical cycle. Sentinel-2A MultiSpectral Instrument (MSI) and Landsat 8 Operational Land Instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) owing to their spatial resolution (10–60 m). In this framework, we assessed the potential of MSI–OLI combined data in characterizing the multi-temporal (2014–2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy). We developed and validated a customized MSI-based TSM model (R2 = 0.81) by exploiting ground measurements acquired during specific measurement campaigns. The model was then exported as OLI data through an intercalibration procedure (R2 = 0.87), allowing for the generation of a TSM multi-temporal MSI–OLI merged dataset. The analysis of the derived multi-year TSM monthly maps showed the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side is more influenced by inflowing rivers and water level fluctuations, the effects of which tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results can be exploited by regional authorities for better management of inland water quality and monitoring systems.

Two tailored configurations of the Robust Satellite Technique (RST) multi-temporal approach, for airborne volcanic ash and desert dust detection, have been tested in the framework of the European Natural Airborne Disaster Information and Coordination System for Aviation (EUNADICS-AV) project. The two algorithms, running on Spinning Enhanced Visible Infra-Red Imager (SEVIRI) data, were previously assessed over wide areas by comparison with independent satellite-based aerosol products. In this study, we present results of a first validation analysis of the above mentioned satellite-based ash/dust products using independent, ground-based observations coming from the European Aerosol Research Lidar Network (EARLINET). The aim is to assess the capabilities of RST-based ash/dust products in providing useful information even at local scale and to verify their applicability as a “trigger” to timely activate EARLINET measurements during airborne hazards. The intense Saharan dust event of May 18–23 2008—which affected both the Mediterranean Basin and Continental Europe—and the strong explosive eruptions of Eyjafjallajökull (Iceland) volcano of April–May 2010, were analyzed as test cases. Our results show that both RST-based algorithms were capable of providing reliable information about the investigated phenomena at specific sites of interest, successfully detecting airborne ash/dust in different geographic regions using both nighttime and daytime SEVIRI data. However, the validation analysis also demonstrates that ash/dust layers remain undetected by satellite in the presence of overlying meteorological clouds and when they are tenuous (i.e., with an integrated backscatter coefficient less than ~0.001 sr−1 and with aerosol backscatter coefficient less than ~1 × 10−6 m−1sr−1). This preliminary analysis confirms that the continuity of satellite-based observations can be used to timely “trigger” ground-based LIDAR measurements in case of airborne hazard events. Finally, this work confirms that advanced satellite-based detection schemes may provide a relevant contribution to the monitoring of ash/dust phenomena and that the synergistic use of (satellite-based) large scale, continuous and timely records with (ground-based) accurate and quantitative measurements may represent an added value, especially in operational scenarios.

The RST (Robust Satellite Techniques)-FLARE algorithm is a satellite-based method using a multitemporal statistical analysis of nighttime infrared signals strictly related to industrial hotspots, such as gas flares. The algorithm was designed for both identifying and characterizing gas flares in terms of radiant/emissive power. The Val d’Agri Oil Center (COVA) is a gas and oil pre-treatment plant operating for about two decades within an anthropized area of Basilicata region (southern Italy) where it represents a significant potential source of social and environmental impacts. RST-FLARE, developed to study and monitor the gas flaring activity of this site by means of MODIS (Moderate Resolution Imaging Spectroradiometer) data, has exported VIIRS (Visible Infrared Imaging Radiometer Suite) records by exploiting the improved spatial and spectral properties offered by this sensor. In this paper, the VIIRS-based configuration of RST-FLARE is presented and its application on the recent (2015-2019) gas flaring activity at COVA is analyzed and discussed. Its performance in gas flaring characterization is in good agreement with VIIRS Nightfire outputs to which RST-FLARE seems to provide some add-ons. The great consistency of radiant heat estimates computed with both RST-FLARE developed configurations allows proposing a multi-sensor RST-FLARE strategy for a more accurate multi-year analysis of gas flaring.

In this work, we assessed the possible relation of ionospheric perturbations observed by Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER), Global Positioning System total electron content (GPS TEC), National Oceanic and Atmospheric Administration (NOAA)-derived outgoing longwave-Earth radiation (OLR), and atmospheric chemical potential (ACP) measurements, with volcanic and Saharan dust events identified by ground and satellite-based medium infrared/thermal infrared (MIR/TIR) observations. The results indicated that the Mt. Etna (Italy) volcanic activity of 2006 was probably responsible for the ionospheric perturbations revealed by DEMETER on 4 November and 6 December and by GPS TEC observations on 4 November and 12 December. This activity also affected the OLR (on 26 October; 6 and 23 November; and 2, 6, and 14 December) and ACP (on 31 October–1 November) analyses. Similarly, two massive Saharan dust episodes, detected by Robust Satellite Techniques (RST) using Spinning Enhanced Visible and Infrared Imager (SEVIRI) optical data, probably caused the ionospheric anomalies recorded, based on DEMETER and GPS TEC observations, over the Mediterranean basin in May 2008. The study confirmed the perturbing effects of volcanic and dust events on tropospheric and ionospheric parameters. Further, it demonstrated the advantages of using independent satellite observations to investigate atmospheric phenomena, which may not always be well documented. The impact of this increased detection capacity in reducing false positives, in the framework of a short-term seismic hazard forecast based on the study of ionospheric and tropospheric anomalies, is also addressed.

Timely and continuous information about flood spatiotemporal evolution are fundamental to ensure an effective implementation of the relief and rescue operations in case of inundation events. In this framework, satellite remote sensing may provide a valuable contribution provided that robust data analysis methods are implemented and suitable data, in terms of spatial, spectral and temporal resolutions, are employed. In this paper, the Robust Satellite Techniques (RST) approach, a satellite-based differential approach, already applied at detecting flooded areas (and therefore christened RST-FLOOD) with good results on different polar orbiting optical sensors (i.e., Advanced Very High Resolution Radiometer – AVHRR – and Moderate Resolution Imaging Spectroradiometer – MODIS), has been fully implemented on time series of Suomi National Polar-orbiting Partnership (Suomi-NPP-SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data. The flooding event affecting the Metaponto Plain in Basilicata and Puglia regions (southern Italy) in December 2013 was selected as a case study and investigated by analysing five years (only December month) of VIIRS Imagery bands at 375 m spatial resolution. The achieved results clearly indicate the potential of the proposed approach, especially when compared with a satellite-based high resolution map of flooded area, as well as with the official flood hazard map of the area and the outputs of a recent published VIIRS-based method. Both flood extent and dynamics have been recognized with good reliability during the investigated period, with only a residual 11.5% of possible false positives over an inundated area extent of about 73 km2. In addition, a flooded area of about 18 km2 was found outside the hazard map, suggesting it requires updating to better manage flood risk and prevent future damages. Finally, the achieved results indicate that medium-resolution optical data, if analysed with robust methodologies like RST-FLOOD, can be suitable for detecting and monitoring floods also in case of small hydrological basins.

In early December 2015, a rapid sequence of strong paroxysmal events took place at the Mt. Etna crater area (Sicily, Italy). Intense paroxysms from the Voragine crater (VOR) generated an eruptive column extending up to an altitude of about 15 km above sea level. In the following days, other minor ash emissions occurred from summit craters. In this study, we present results achieved by monitoring Mt. Etna plumes by means of RSTASH (Robust Satellite Techniques-Ash) algorithm, running operationally at the Institute of Methodologies for Environmental Analysis (IMAA) on Advanced Very High Resolution Radiometer (AVHRR) data. Results showed that RSTASH detected an ash plume dispersing from Mt. Etna towards Ionian Sea starting from 3 December at 08:40 UTC, whereas it did not identify ash pixels on satellite data of same day at 04:20 UTC and 04:40 UTC (acquired soon after the end of first paroxysm from VOR), due to a mixed cloud containing SO2 and ice. During 8–10 December, the continuity of RSTASH detections allowed us to estimate the mass eruption rate (an average value of about 1.5 × 103 kg/s was retrieved here), quantitatively characterizing the eruptive activity from North East Crater (NEC). The work, exploiting information provided also by Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, confirms the important contribution offered by RSTASH in identifying and tracking ash plumes emitted from Mt. Etna, despite some operational limitations (e.g., cloud coverage). Moreover, it shows that an experimental RST product, tailored to SEVIRI data, for the first time used and preliminarily assessed here, may complement RSTASH detections providing information about areas mostly affected by volcanic SO2.

A wave-propelled autonomous vehicle (Wave Glider) instrumented with a variety of oceanographic and meteorological sensors was launched from Gulf of Naples on the 12th of September 2012 for a two-week mission in the Southern Tyrrhenian Sea. The main objective of the mission was a preliminary evaluation of the potential of commercial autonomous platforms to provide reliable measurements of sea surface parameters which can complement existing satellite based products moving from the local to the synoptic scale. To this aim Wave Glider measurements were compared to equivalent, or near-equivalent, satellite products achieved from MODIS (Moderate Resolution Imaging Spectroradiometer) sensors onboard the EOS (Earth Observing System) satellite platforms and from AVISO (Archiving Validation and Interpretation of Satellite Oceanographic Data). Level-3 near real time and Level-4 reprocessed sea surface foundation temperature products provided by the CMEMS (Copernicus Marine Environment Monitoring Service) were also included in this study as well as high resolution model output supplied by NEMO (Nucleus for European Modelling of the Ocean). The Wave Glider was equipped with sensors to measure temperature, salinity, currents, as well as Colored Dissolved Organic Matter (CDOM), turbidity and refined fuels fluorescence. The achieved results confirmed the emerging value of Wave Gliders in the framework of multiplatform monitoring systems of the ocean surface parameters. In particular, they showed that Wave Glider measurements captured the southern Tyrrhenian Sea major surface oceanographic features, including the coast to open sea haline gradient and the presence of a cyclone-anticyclone system in the southeastern sub-region. The Wave Glider also had the capability to monitor upper ocean currents at finer spatial and temporal scales than satellite altimetric observations and model outputs. Nonetheless, results stressed the existence of several limits in the combined use of satellite and Wave Glider observations and the necessity of further analyses concerning the monitoring of the ocean optical properties. In fact, Wave Glider and satellite-based products agree in terms of sea surface temperature and currents patterns, while bio-optical properties turned out to be less well correlated. No significant traces of refined fuels have been detected along the WG track.

In May 2016, three powerful paroxysmal events, mild Strombolian activity, and lava emissions took place at the summit crater area of Mt. Etna (Sicily, Italy). During, and immediately after the eruption, part of the North-East crater (NEC) collapsed, while extensive subsidence affected the Voragine crater (VOR). Since the end of the May eruptions, a diffuse fumarolic activity occurred from a fracture system that cuts the entire summit area. Starting from 7 August, a small vent (of ~20⁻30 m in diameter) opened up within the VOR crater, emitting high-temperature gases and producing volcanic glow which was visible at night. We investigated those volcanic phenomena from space, exploiting the information provided by the satellite-based system developed at the Institute of Methodologies for Environmental Analysis (IMAA), which monitors Italian volcanoes in near-real time by means of the RSTVOLC (Robust Satellite Techniques⁻volcanoes) algorithm. Results, achieved integrating Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) observations, showed that, despite some issues (e.g., in some cases, clouds masking the underlying hot surfaces), RSTVOLC provided additional information regarding Mt. Etna activity. In particular, results indicated that the Strombolian eruption of 21 May lasted longer than reported by field observations or that a short-lived event occurred in the late afternoon of the same day. Moreover, the outcomes of this study showed that the intensity of fumarolic emissions changed before 7 August, as a possible preparatory phase of the hot degassing activity occurring at VOR. In particular, the radiant flux retrieved from MODIS data decreased from 30 MW on 4 July to an average value of about 7.5 MW in the following weeks, increasing up to 18 MW a few days before the opening of a new degassing vent. These outcomes, in accordance with information provided by Sentinel-2 MSI (Multispectral Instrument) and Landsat 8-OLI (Operational Land Imager) data, confirm that satellite observations may also contribute greatly to the monitoring of active volcanoes in areas where efficient traditional surveillance systems exist.

The last decades have seen a massive advance in technologies for Earth observation (EO) and environmental monitoring, which provided scientists and engineers with valuable spatial information for studying hydrologic processes. At the same time, the power of computers and newly developed algorithms have grown sharply. Such advances have extended the range of possibilities for hydrologists, who are trying to exploit these potentials the most, updating and re-inventing the way hydrologic and hydraulic analyses are carried out. A variety of research fields have progressed significantly, ranging from the evaluation of water features, to the classification of land-cover, the identification of river morphology, and the monitoring of extreme flood events. The description of flood processes may particularly benefit from the integrated use of recent algorithms and monitoring techniques. In fact, flood exposure and risk over large areas and in scarce data environments have always been challenging topics due to the limited information available on river basin hydrology, basin morphology, land cover, and the resulting model uncertainty. The ability of new tools to carry out intensive analyses over huge datasets allows us to produce flood studies over large extents and with a growing level of detail. The present Special Issue aims to describe the state-of-the-art on flood assessment, monitoring, and management using new algorithms, new measurement systems and EO data. More specifically, we collected a number of contributions dealing with: (1) the impact of climate change on floods; (2) real time flood forecasting systems; (3) applications of EO data for hazard, vulnerability, risk mapping, and post-disaster recovery phase; and (4) development of tools and platforms for assessment and validation of hazard/risk models.

Timely and continuous information about flood dynamics are fundamental to ensure an effective implementation of the relief and rescue operations. Satellite data provided by optical sensors onboard meteorological satellites could have great potential in this framework, offering an adequate trade-off between spatial and temporal resolution. The latest would benefit from the integration of observations coming from different satellite systems, also helping to increase the probability of finding cloud free images over the investigated region. The Robust Satellite Techniques for detecting flooded areas (RST-FLOOD) is a sensor-independent multi-temporal approach aimed at detecting flooded areas which has already been applied with good results on different polar orbiting optical sensors. In this work, it has been implemented on both the 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375 m Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The flooding event affecting the Basilicata and Puglia regions (southern Italy) in December 2013 has been selected as a test case. The achieved results confirm the RST-FLOOD potential in reliably detecting, in case of small basins, flooded areas regardless of the sensor used. Flooded areas have indeed been detected with similar performance by the two sensors, allowing for their continuous and near-real time monitoring.

The identification of subtle thermal anomalies (i.e., of low-temperature and/or spatial extent) at volcanoes by satellite is of great interest for scientists, especially because minor changes in surface temperature might reveal an unrest phase or impending activity. A good test case for assessing the sensitivity level of satellite-based methods is to study the thermal activity of Oldoinyo Lengai (OL) (Africa, Tanzania), which is the only volcano on Earth emitting natrocarbonatite lavas at a lower temperature (i.e., in the range 500–600 °C) than usual magmatic surfaces. In this work, we assess the potential of the RSTVOLC multi-temporal algorithm in detecting subtle hot spots at OL for comparison with MODLEN: A thermal anomaly detection method tailored to OL local conditions, by using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Our results investigating the eruptive events of 2000–2008 using RSTVOLC reveal the occurrence of several undocumented thermal activities of OL, and may successfully integrate MODLEN observations. In spite of some known limitations strongly affecting the identification of volcanic thermal anomalies from space (e.g., cloud cover; occurrence of short-lived events), this work demonstrates that RSTVOLC may provide a very important contribution for monitoring the OL, identifying subtle hot spots showing values of the radiant flux even around 1 MW.

An effective characterization of gas flaring is hampered by the lack of systematic, complete and reliable data on its magnitude and spatial distribution. In the last years, a few satellite methods have been developed to provide independent information on gas flaring activity at global, national and local scale. Among these, a MODIS-based method, aimed at the computation of gas flared volumes by an Italian plant, was proposed. In this work, a more general version of this approach, named RST-FLARE, has been developed to provide reliable information on flaring sites localization and gas emitted volumes over a long time period for the Niger Delta region, one of the top five gas flaring areas in the world. Achieved results showed a good level of accuracy, in terms of flaring sites localization (95% of spatial match) and volume estimates (mean bias between in 16% and 20%, at annual scale and 2–9% in the long period) when compared to independent data, provided both by other satellite techniques and national/international organizations. Outcomes of this work seem to indicate that RST-FLARE can be used to provide, at different geographic scales, quite accurate data on gas flaring, suitable for monitoring purposes for governments and local authorities.

The Agung volcano (Bali; Indonesia) erupted in later November 2017 after several years of quiescence. Because of ash emissions, hundreds of flights were cancelled, causing an important air traffic disruption in Indonesia. We investigate those ash emissions from space by applying the RSTASH algorithm for the first time to Himawari-8 data and using an ad hoc implementation scheme to reduce the time of the elaboration processes. Himawari-8 is a new generation Japanese geostationary meteorological satellite, whose AHI (Advanced Himawari Imager) sensor offers improved features, in terms of spectral, spatial and temporal resolution, in comparison with the previous imagers of the MTSAT (Multi-Functional Transport Satellite) series. Those features should guarantee further improvements in monitoring rapidly evolving weather/environmental phenomena. Results of this work show that RSTASH was capable of successfully detecting and tracking the Agung ash plume, despite some limitations (e.g., underestimation of ash coverage under certain conditions; generation of residual artefacts). Moreover, estimates of ash cloud-top height indicate that the monitored plume extended up to an altitude of about 9.3 km above sea level during the period 25 November at 21:10 UTC–26 November at 00:50 UTC. The study demonstrates that RSTASH may give a useful contribution for the operational monitoring of ash clouds over East Asia and the Western Pacific region, well exploiting the 10 min temporal resolution and the spectral features of the Himawari-8 data.

Standard chlorophyll-a (chl-a) algorithms, which rely on Moderate Resolution Imaging Spectro-radiometer (MODIS) data aboard the Aqua satellite, usually show different performances depending on the area under consideration. In this paper, we assessed their accuracy in retrieving the chl-a concentration in the Basilicata Ionian Coastal waters (Ionian Sea, South of Italy). The outputs of one empirical (Med-OC3) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, have been compared with ground measurements acquired during three different measurement campaigns. The achieved results prove the poor accuracy (adjusted R2 value of 0.12) of the investigated empirical algorithm and, conversely, the good performance of semi-analytical algorithms (adjusted R2 ranging from 0.74 to 0.79). The co-existence of Coloured Dissolved Organic Matter (CDOM) and Non-Algal Particles (NAP) has likely determined large errors in the reflectance ratios used in the OCx form algorithms. Finally, a local scale assessment of the bio-optical properties, on the basis of the in situ dataset, allowed for the definition of an operational local scale-tuned version of the MODIS chl-a algorithm, which assured increased accuracy (adjusted R2 value of 0.86). Such a tuned algorithm version can provide useful information which can be used by local authorities within regional management systems.

Satellite remote sensing has increasingly become a crucial tool for volcanic activity monitoring thanks to continuous observations at global scale, provided with different spatial/spectral/temporal resolutions, on the base of specific satellite platforms, and at relatively low costs. Among the satellite techniques developed for volcanic activity monitoring, the RST (Robust Satellite Techniques) approach has shown high performances in detecting hot spots as well as in automatically identifying ash plumes, effectively discriminating them from weather clouds. This method, based on an extensive, multi-temporal analysis of long-term time series of homogeneous satellite records, has recently been implemented on EOS-MODIS and MSG-SEVIRI data for which further performance improvements are expected. These satellite systems, in fact, offer improved spectral and/or temporal resolutions. In this paper, some preliminarily results of these analyses are presented, both regarding hot spot identification and ash cloud detection and tracking. The potential of RST, to be used within early warning systems devoted to volcanic hazard monitoring and mitigation, will also be discussed.

Dust outbreaks are meteorological phenomena of great interest for scientists and authorities (because of their impact on the climate, environment, and human activities), which may be detected, monitored, and characterized from space using different methods and procedures. Among the recent dust detection algorithms, the RSTDUST multi-temporal technique has provided good results in different geographic areas (e.g., Mediterranean basin; Arabian Peninsula), exhibiting a better performance than traditional split window methods, in spite of some limitations. In this study, we present an optimized configuration of this technique, which better exploits data provided by Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard Meteosat Second Generation (MSG) satellites to address those issues (e.g., sensitivity reduction over arid and semi-arid regions; dependence on some meteorological clouds). Three massive dust events affecting Europe and the Mediterranean basin in May 2008/2010 are analysed in this work, using information provided by some independent and well-established aerosol products to assess the achieved results. The study shows that the proposed algorithm, christened eRSTDUST (i.e., enhanced RSTDUST), which provides qualitative information about dust outbreaks, is capable of increasing the trade-off between reliability and sensitivity. The results encourage further experimentations of this method in other periods of the year, also exploiting data provided by different satellite sensors, for better evaluating the advantages arising from the use of this dust detection technique in operational scenarios.

Natural crude-oil seepages, together with the oil released into seawater as a consequence of oil exploration/production/transportation activities, and operational discharges from tankers (i.e., oil dumped during cleaning actions) represent the main sources of sea oil pollution. Satellite remote sensing can be a useful tool for the management of such types of marine hazards, namely oil spills, mainly owing to the synoptic view and the good trade-off between spatial and temporal resolution, depending on the specific platform/sensor system used. In this paper, an innovative satellite-based technique for oil spill detection, based on the general robust satellite technique (RST) approach, is presented. It exploits the multi-temporal analysis of data acquired in the visible channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite in order to automatically and quickly detect the presence of oil spills on the sea surface, with an attempt to minimize “false detections” caused by spurious effects associated with, for instance, cloud edges, sun/satellite geometries, sea currents, etc. The oil spill event that occurred in June 2007 off the south coast of Cyprus in the Mediterranean Sea has been considered as a test case. The resulting data, the reliability of which has been evaluated by both carrying out a confutation analysis and comparing them with those provided by the application of another independent MODIS-based method, showcase the potential of RST in identifying the presence of oil with a high level of accuracy.

Monitoring river plume dynamics and variations in complex coastal areas can provide useful information to prevent marine environmental damage. In this work, the Robust Satellite Techniques (RST) approach has been implemented and tested on historical series of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor, for the first time, Suspended Particulate Matter (SPM) anomalies associated to river plumes. To this aim, MODIS-Aqua Level 1A data were processed using an atmospheric correction adequate for coastal waters, and SPM daily maps were generated applying an algorithm adapted from literature. The RST approach was then applied to these maps to assess the anomalous presence of SPM. The study area involves the Basilicata region coastal waters (Ionian Sea, South of Italy). A long-time analysis (2003–2015) conducted for the month of December allows us to find that the maximum SPM concentration value was registered in December 2013, when an extreme hydrological event occurred. A short-time analysis was then carried out applying RST to monitor the dynamics of anomalous SPM concentrations. Finally, the most exposed areas, in terms of SPM concentration, were identified. The results obtained in this work showed the RST high potential when used in combination with standard SPM daily maps to better characterize and monitor coastal waters.

An optimized configuration of the Robust Satellite Technique (RST) approach was developed within the framework of the ‘LAVA’ project. This project is funded by the Italian Department of Civil Protection and the Italian Istituto Nazionale di Geofisica e Vulcanologia, with the aim to improve the effectiveness of satellite monitoring of thermal volcanic activity. This improved RST configuration, named RSTVOLC, has recently been implemented in an automatic processing chain that was developed to detect hot-spots in near real-time for Italian volcanoes. This study presents the results obtained for the Mount Etna eruption of July 14-24, 2006, using the Moderate Resolution Imaging Spectroradiometer (MODIS) data. To better assess the operational performance, the RSTVOLC results are also discussed in comparison with those obtained by MODVOLC, a well-established, MODIS-based algorithm for hot-spot detection that is used worldwide.