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The 1980 Ms 6.9 Irpinia earthquake was responsible for the activation or reactivation of numerous gravitative deformations mainly hosted by clayey lithotypes, affecting wide areas of Benevento Province and the Sele and Ofanto R. Valleys. The case of Calitri offers valuable insights into a methodological approach to studying mass movements affecting human settlements. Post-earthquake investigations in Calitri involved extensive geognostic boreholes and in situ surveys, providing substantial data for lithological characterization and landslide modeling. Additionally, over the past two decades, satellite-based techniques have supported the mapping and characterization of ground deformations in this area, improving our understanding of spatiotemporal evolution. Despite these efforts, a detailed subsurface comprehensionof the tectono-stratigraphy and geometriesof gravity-induced deformation remains incomplete. This study aims to enhance our knowledge of gravity-driven deformations affecting urban areas by using deep-penetrating GroundPenetrating Radar (GPR) surveys to identify landslide-related structures, rupture surfaces, and lithological characterization of the involved lithotypes. The integration of GPR surveys with classical morphotectonic analysis led to the delineation of the main subsurface discontinuities (stratigraphy, tectonics, and gravity-related), correlating them with available geognostic data. This approach provided non-invasive, detailed insights into subsurface features and stands out as one of the rare case studies in Italy that employed the GPR method for landslide investigations.

This study is an efficiency comparison between four methods for the production of landslide susceptibility maps (LSMs), which include random forest (RF), artificial neural network (ANN), and logistic regression (LR) as the machine learning (ML) techniques and frequency ratio (FR) as a statistical method. The study area is located in the Southern Hiroshima Prefecture in western Japan, a locality known to suffer from rainfall-induced landslide disasters, the most recent one in July 2018. The landslide conditioning factors (LCFs) considered in this study are lithology, land use, altitude, slope angle, slope aspect, distance to drainage, distance to lineament, soil class, and mean annual precipitation. The rainfall LCF data comprise XRAIN (eXtended RAdar Information Network) radar records, which are novel in the task of LSM production. The accuracy of the produced LSMs was calculated with the area under the receiver operating characteristic curve (AUROC), and an automatic hyperparameter tuning and result comparison system based on AUROC scores was utilized. The calculated AUROC scores of the resulting LSMs were 0.952 for the RF method, 0.9247 for the ANN method, 0.9016 for the LR method, and 0.8424 for the FR. It is also noteworthy that the ML methods are substantially swifter and more practical than the FR method and allow for multiple and automatic experimentations with different hyperparameter settings, providing fine and accurate outcomes with the given data. The results evidence that ML techniques are more efficient when dealing with hazard assessment problems such as the one exemplified in this study. Although the conclusion that the RF method is the most accurate for LSM production as found by other authors in the literature, ML method efficiency may vary depending on the specific study area, and thus the use of an automatic multi-method LSM production system with hyperparameter tuning such as the one utilized in this study is advised. It was also found that XRAIN radar-acquired mean annual precipitation data are effective when used as an LCF in LSM production.

The single polarization C-Band weather radar network of the Meteorological Service of Catalonia covers the entire region (32,000 km2), which allows it to apply a series of corrections that improve preliminary estimations of the rainfall field (hourly and daily). In addition, an automatic re-processing using automatic weather stations helps to incorporate ground-based information. The last process of the quantitative precipitation estimation (QPE) is running the end-product again eight days later, when the data have been reviewed and corrected in the case of detecting anomalies in the radar or gauge data. These corrections are applied operationally, with the fields generated and stored automatically. The QPE fields are generated in the GeoTIFF format, allowing easy use with multiple applications and simplifying processes such as quality control. In this way, the analysis of a 10 year period of GeoTIFF QPE daily data compared with ground rainfall values is introduced. The results help to understand different points regarding the functioning of the network such as the dependance on the type of precipitation and the seasonality. In addition, the description of a heavy rainfall episode (22 October 2019) shows the variations and improvements in the different products. The main conclusions refer to how using GeoTIFF combined with point data (rain gauges), it is possible to ensure simple but effective quality control of an operational radar network.

Using ground penetrating radar (GPR) we investigate the near surface (~0–10 m depth) geophysical structure of neotectonic fault-propagation folds and thrust faults in south-central Australia in varying stages of fold and fault growth. Variations in neotectonic fold scarp heights are interpreted to reflect variations in accumulated slip on the underlying reverse faults. Fold scarps on the Nullarbor and Roe Plains are characterized by broad, asymmetric morphologies with vertical displacements of ~5 to ~40 m distributed over 1 to 2 km widths (~0.5 to ~4 m per 100 m). Within increasing scarp height there is an increase in the frequency and spatial density of strong reflector packages in the hanging wall that are attributed to material contrasts imposed by co-seismic fracturing and associated lithological and weathering variations. No evidence for discrete faulting is found at scarp heights up to 40 m (maximum relief of 4 m per 100 m). Where the principal slip zone of a fault ruptures to the surface, scarp morphologies are characterized by steep gradients (ca. 10 m per 100 m). Discrete faulting is imaged in GPR as structural lineaments, abrupt changes in the thickness of reflector packages with variations of amplitude, and/or hyperbolic diffraction packages indicative of the disturbance of reflector packages. Geophysical imaging of subtle changes in the shallow geological structure during growth of fault-propagation folds can be conducted using GPR informing the identification of locations for invasive investigations (e.g., trenching).

The detection of water leakage along its transportation network has important societal impacts, such as avoiding a large volume of water wasted along the waterways or preventing water-related chemical or physical surrounding media deterioration. Among the vast domain of destructive techniques, Ground-Penetrating Radar (GPR) is a common and efficient tool used for detection in many near-surface contexts, and it is particularly efficient in civil engineering cases, such as utility detection, due to its fine resolution and the ease of data acquisition. A peculiar form of signal enhancement appears in GPR profiles recorded over spheres and cylinders where velocity contrasts exist between the body’s material and the surrounding medium. We used this enhancement to detect potential water leakages in water pipes. After exhibiting the signal enhancement effect in a laboratory sandbox experiment using a spherical glass ball, we verified the results with numerical experiments with varied sphere and cylinder sizes and dielectric properties. We then investigated field and numerical experiments of GPR transects above a “real life” water-leaking PVC pipe. Our results show that the water cylinder and water infiltration bulb produced a characteristic signal that could be used for detecting water leakages along water pipes. The largest amplitude in the GPR signal is caused by a bottom pipe reflection enhanced by the water bulb and not by the top of the pipe. We stress the risk of miscalculating the pipe’s depth during velocity estimation when amplitude enhancement conditions are met. Beyond civil-engineering impacts, knowledge on signal amplification phenomena can help GPR data interpretations in sedimentology and hydrogeology studies.

This study focuses on analyzing and monitoring urban subsidence, particularly in the city of Palermo, Italy. Land subsidence, induced by natural and human factors, poses threats to infrastructure and urban safety. Remote sensing (RS), specifically synthetic-aperture radar interferometry (In-SAR), is employed due to its ability to detect ground displacements over large areas with great precision. The persistent scatterer InSAR (PS-InSAR) technique is utilized to identify stable targets and track millimeter-level surface deformations. This research spans from October 2014 to October 2021, using Sentinel-1 satellite data to capture ground deformation from various angles. The findings are integrated into an accessible web app (ArcGIS) for local authorities that could be used aiding in urban planning and enhancing safety measures. This study’s results offer updated deformation maps, serving as an operational tool to support decision-making and community resilience, emphasizing risk awareness and responsible practices. This study highlights that the exponential expansion of urban areas, which does not take into account historical information, can gravely jeopardize both the integrity of urban infrastructure and the well-being of its inhabitants. In this context, remote sensing technologies emerge as an invaluable ally, used in monitoring and safeguarding the urban landscape.

Geohazards, such as earthquakes, pose significant threats to human life and infrastructure in various regions across the globe. Iran, in particular, is highly vulnerable to earthquakes due to its unique structural and tectonic characteristics. Therefore, it is crucial to monitor geohazards in order to mitigate their impacts. Several techniques, including the global positioning system, geodesy, tacheometry, and mapping cameras, have been developed for this purpose. Among these methods, radar interferometry has emerged as a particularly accurate and cost-effective approach. It is capable of operating under all weather conditions, 24 h a day, and can cover large areas with high spatial and temporal resolution. In this research, we employed Sentinel 1A images and radar interferometry to investigate the changes in the Earth’s surface following earthquakes in the marginal strip of the Persian Gulf. Specifically, our focus was on earthquakes in Bandar Khamir, and we analyzed the Earth’s surface changes three days and fifteen days after the events. The findings of our study revealed that the most significant uplift occurred around Bandar Khamir, with an uplift rate of 14 cm. Conversely, the highest subsidence was observed near Bandar Charak, with a subsidence of 12 cm. Furthermore, we observed a rise of 32 cm around the eastern and northern regions of Bandar Khamir 11 days after the initial period, accompanied by a subsidence of 31 cm around Bandar Lange and Bandar Charak. These results underscore the importance of continuous monitoring of earthquakes and their impact on the Earth’s surface, particularly in coastal areas where the effects on the water table and coastal infrastructure can be severe. In conclusion, this study highlights the significance of employing radar interferometry as a powerful tool for monitoring and assessing the impacts of earthquakes.

In this study, we use GIS and other analytical platforms to analyze the landslide distribution pattern in the July 2018 heavy rain disasters in the southern part of Hiroshima Prefecture in Japan in conjunction with chronological XRAIN (eXtended Radar Information Network) radar-acquired localized rainfall data in order to better understand the relationship between rainfall characteristics and landslide probability. An analysis of event rainfall from the July 2018 disasters determines that landslide-inducing rainfall started from 8:30 AM on 5 July and continued until 7:30 AM on 7 July, accumulating to up to 368 mm in total precipitation, and that there were two intensity peaks, one around 7:30 PM on 6 July, and another one around 4:30 AM on 7 July. These two events are associated with particularly high landslide activity, which indicates that landslide activation is related to peak-intensity rainfall combined with accumulated continuous precipitation. The XRAIN data were also used together with landslide reports to calculate the intensity–duration (i.e., I-D) rainfall threshold for the area. The mean annual precipitation in the whole study area ranged between 2025 mm and 3030 mm, with an average value of about 2300 mm. The spatial distribution of rainfall throughout the sampled years indicates that rainfall is remarkably localized, with higher values concentrated on elevated areas. However, it was also observed that the maximum precipitation volumes are not so closely related to landslide occurrence, and the highest landslide activity was found in intermediate precipitation class zones instead. Correlating the localization patterns of event precipitation and mean annual precipitation using Pearson’s correlation coefficient, we found an r value of 0.55, which is considered a moderate correlation between the two datasets (i.e., event precipitation and mean annual precipitation).

The study presents results from applying the Real Aperture Radar interferometry technique and Digital Image Correlation through a mobile phone camera to identify static and dynamic deformations of a gantry during surveying operations on the Michelangelo’s David at the Galleria dell’Accademia di Firenze Museum in Florence. The statue has considerable size and reaches an elevation of more than seven meters on its pedestal. An ad-hoc gantry was designed and deployed, given the cramped operating area around the statue. The scanner had a stability control system that forbid surveying in instrument movements. However, considering the unicity of the survey and its rare occurrence, the previous survey had been carried out in the year 2000; verifying stability and recording deformations is a crucial task, and necessary for validation. As the gantry does not have an on-board stability sensor, and considering the hi-survey accuracy requested, a redundant, contactless, remote monitoring system of the gantry and the statue stability was chosen to guarantee the maximum freedom of movement around the David to avoid any interference during scanning operations. Thanks to the TInRAR technique, the gantry and the statue were monitored with an accuracy of 0.01 mm. At the same time, a Digital Image Correlation analysis was performed on the gantry, which can be considered a Multi-Degree-Of-Freedom (MDOF) system, to accurately calculate the vibration frequency and amplitude. A comparison between TInRAR and DIC results reported substantial accordance in detecting gantry’s oscillating frequencies; a predominant oscillation frequency of 1.33 Hz was identified on the gantry structure by TinSAR and DIC analysis.

Very short-term (0~3 h) radar-based quantitative precipitation forecasting (QPF), also known as nowcasting, plays an essential role in flash flood warning, water resource management, and other hydrological applications. A novel nowcasting method combining radar data and a model wind field was developed and validated with two hurricane precipitation events. Compared with several existing nowcasting approaches, this work attempts to enhance the prediction capabilities from two major aspects. First, instead of using a radar reflectivity field, this work proposes the use of the rainfall rate field estimated from polarimetric radar variables in the motion field derivation. Second, the derived motion field is further corrected by the Rapid Refresh (RAP) model field. With the corrected motion field, the future rainfall rate field is predicted through a linear extrapolation method. The proposed method was validated using two hurricanes: Harvey and Irma. The proposed work shows an enhanced performance according to statistical scores. Compared with the model only and centroid-tracking only approaches, the average probability of detection (POD) increases about 25% and 50%; the average critical success index (CSI) increases about 20% and 37%; and the average false alarm rate (FAR) decreases about 14% and 16%, respectively.

Common industry practice means that geological or stratigraphic boundaries are estimated from exploration drill holes. While exploration holes provide opportunities for accurate data at a high resolution down the hole, their acquisition is cost-intensive, which can result in the number of holes drilled being reduced. In contrast, sampling with ground-penetrating radar (GPR) is cost-effective, non-destructive, and compact, allowing for denser, continuous data acquisition. One challenge with GPR data is the subjectivity and challenges associated with interpretation. This research presents a hybrid model of geologist and machine learning for the identification of geological boundaries in a lateritic deposit. This model allows for an auditable, probabilistic representation of geologists’ interpretations and can feed into exploration planning and optimising drill campaigns in terms of the density and location of holes.

We study the surface deformation following a moderate size M5+ earthquake sequence that occurred close to Tyrnavos village (Thessaly, Greece) in March 2021. We adopt the interferometric synthetic aperture radar (InSAR) technique to exploit several pairs of Sentinel-1 acquisitions and successfully retrieve the ground movement caused by the three major events (M5+) of the sequence. The mainshocks occurred at depths varying from ~7 to ~10 km, and are related to the activation of at least three normal faults characterizing the area previously unknown. Thanks to the 6-day repeat time of the Sentinel-1 mission, InSAR analysis allowed us to detect both the surface displacement due to the individual analyzed earthquakes and the cumulative displacement caused by the entire seismic sequence. Especially in the case of a seismic sequence that occurs over a very short time span, it is quite uncommon to be able to separate the surface effects ascribable to the mainshock and the major aftershocks because the time frequency of radar satellite acquisitions often hamper the temporal separation of such events. In this work, we present the results obtained through the InSAR data analysis, and are able to isolate single seismic events that were part of the sequence.

Ground-penetrating radar and crosshole radar are applied in an underground marble mine for fault detection and to test if different geological bodies can be distinguished. Boreholes are often drilled in advance of mining to clarify the locations of ore bodies and gangues. Here, such boreholes were used for crosshole investigations to supplement optical borehole imaging. Four boreholes were drilled along a profile with increasing offsets from 5 to 25 m. The crosshole measurements were performed with 100 MHz antennas. Tomographic panels were created up to a depth of 28 m and were complemented by reflection mode ground-penetrating radar (GPR) measurements along a 25 m-long profile with 100 and 250 MHz antennas. The GPR imaging successfully delineates the fault and karstification zones with higher water content due to their strong dielectric permittivity contrast compared to the surrounding geology.

The internal geometries of progradational units on the coastal plain in southeastern Spain (Carchuna Beach, Granada) since the maximum Holocene marine transgression (ca. 7000 cal BP) have been analysed using ground-penetrating radar (GPR). Radargram analysis shows the presence of sedimentary structures consistent with the progradation of coastal plains, interpreted as laminations associated with the foreset and topset. Laterally, five middle scale progradational units (H2, H3, H4, H5 and H6) are identified. Each of these units can be grouped into two major units. The general trend towards progradation is interrupted at the boundary of the units. Minor scale units (prograding wedges), deposited at topographically lower areas, are detected in GPR profiles. The radargrams also show channel erosional surfaces and sigmoidal landward dipping reflections interpreted as washover channel deposits, with the erosion and transference of sediments between the beach and the back-ridge domain. Furthermore, the saturation of coastline sediments with saltwater rapidly attenuates the GPR signal. Thus, the GPR is also a good tool to detect saltwater intrusions in shallow aquifers.

We investigate how suitable ground penetrating radar (GPR) and geoelectrics are to prospect the remains of submerged wooden archaeological constructions in the water column. For this purpose, we determined the contrasts of electric resistivity and dielectric permittivity from measurements on present-day wood samples, serving as simplified approximations of water saturated and undegraded archaeological wood. As common substitutes of hard and soft construction wood, we investigated oak and spruce wood. The electric resistivity and dielectric permittivity were determined with increasing moisture content from small-scale electric and GPR measurements using a Wenner alpha array and a 2 GHz Palm antenna in a watering experiment. In a water-saturated state, resistivity values of 20 were observed. The anisotropy effects and deviations of the wood species were seen to be up to 30%. On the basis of this, the relative material contrasts of wood with respect to fresh water, sand, and clay were calculatedand compared to values found in the literature for seismic contrasts. Geoelectric, GPR, and seismic measurements show contrasts of 0.3 to 0.8, −0.4 to 0.2, and −0.24 to 0.35, depending on the surrounding material and structural orientation of the wood. The highest contrasts were found for wood in fresh water, followed by clayey and sandy subsoils. On the basis of the determined contrasts, analytical calculations were performed showing that an object of 0.5 m diameter can be detected at depths between 0.5 m and 1.5 m with geoelectrics (Schlumberger) and at depths between 0.5 m and 3 m with ground penetrating radar measurements (400 MHz).

Depth-integrated simulations of snow avalanches have become a central part of risk analysis and mitigation. However, the common practice of applying different model parameters to mimic different avalanches is unsatisfying. In here, we analyse this issue in terms of two differently sized avalanches from the full-scale avalanche test-site Vallée de la Sionne, Switzerland. We perform depth-integrated simulations with the toolkit OpenFOAM, simulating both events with the same set of model parameters. Simulation results are validated with high-resolution position data from the GEODAR radar. Rather than conducting extensive post-processing to match radar data to the output of the simulations, we generate synthetic flow signatures inside the flow model. The synthetic radar data can be directly compared with the GEODAR measurements. The comparison reveals weaknesses of the model, generally at the tail and specifically by overestimating the runout of the smaller event. Both issues are addressed by explicitly considering deposition processes in the depth-integrated model. The new deposition model significantly improves the simulation of the small avalanche, making it starve in the steep middle part of the slope. Furthermore, the deposition model enables more accurate simulations of deposition patterns and volumes and the simulation of avalanche series that are influenced by previous deposits.

Data from geocryological studies of soil and rock massifs in permafrost zone are very important as a basis for predicting possible negative consequences associated with climate change. A promising technique for studying geocryological structures (various types of underground ice) is the ground-penetrating radar (GPR) method. This paper presents the applications of the GPR method to prospect and evaluate massive ice in a frozen rock mass. To study the features of GPR signals received during sounding of underground ice, a model of a single GPR trace for the structure “frozen rock-ice-frozen rock” was developed. As a result, regularities were established in the kinematic and dynamic characteristics of GPR signals at the upper and lower boundaries of massive ice, depending on its geometric parameters. The established features were confirmed by the results of computer and physical simulation of GPR measurements of a frozen rock mass model. The main result of the study was to obtain a set of criteria for identifying massive ice according to GPR measurements. The developed criteria will allow the use of GPR for a detailed study of the structure of permafrost rocks to prevent the development of dangerous cryogenic processes in undisturbed and urban areas of the Arctic.

Hand-held ground penetrating radar (GPR) systems have been widely applied to landmine detections during recent decades. The accuracy of an imaging result by migration for a hand-held GPR is strongly related to the accuracy of subsurface velocity distribution obtained from multi offset data. For shallow targets like landmines, the hyperbolas are usually not distinct in 2D slices and are masked by the surface reflections. In this article, we propose a 3D migration depth focus velocity analysis method for hand-held GPRs to estimate the background velocity of the subsurface. This method is performed based on the images generated by migrations. The objective function is defined as the proportion of the target on the depth slice containing the target. After migrating a GPR radargram with different velocities, the background velocity, which minimizes the objective function, can be determined by comparing the imaging results by migration using different velocities. To test the proposed method, we apply this procedure to experimental GPR data collected with an advanced landmine imaging system (ALIS) in the laboratory. Subsequently, the velocity of the background is obtained, 3D diffraction migration with the obtained velocity achieves subsurface imaging with high quality. The accurate position and depth of the target are obtained from the optimal migration image.

The monitoring, modeling, and prediction of storm events and accompanying heavy rain is crucial for intensively used agricultural landscapes and its settlements and transport infrastructure. In Saxony, Germany, repeated and numerous storm events triggered muddy floods from arable fields in May 2016. They caused severe devastation to settlements and transport infrastructure. This interdisciplinary approach investigates three muddy floods, which developed on silty soils of loess origin tending to soil surface sealing. To achieve this, the study focuses on the test of a historical forecast modeling of three muddy floods in ungauged agricultural landscapes. Therefore, this approach firstly illustrates the reconstruction of the muddy floods, which was performed by high-resolution radar precipitation data, physically-based erosion modeling, and the qualitative validation by unmanned aerial vehicle-based orthophotos. Subsequently, historical radar precipitation forecasts served as input data for the physically-based erosion model to test the forecast modeling retrospectively. The model results indicate a possible warning for two of the three muddy floods. This method of a historical forecast modeling of muddy floods seems particularly promising. Naturally, the data series of three muddy floods should be extended to more reliable data and statistical statements. Finally, this approach assesses the feasibility of a real-time muddy flood early warning system in ungauged agricultural landscapes by high-resolution radar precipitation forecasts and physically-based erosion modeling.

Storm events and accompanying heavy rain endanger the silty soils of the fertile and intensively-used agricultural landscape of the Saxon loess province in the European loess belt. In late spring 2016, persistent weather conditions with repeated and numerous storm events triggered flash floods, landslides, and mud flows, and caused severe devastation to infrastructure and settlements throughout Germany. In Saxony, the rail service between Germany and the Czech Republic was disrupted twice because of two mud flows within eight days. This interdisciplinary study aims to reconstruct the two mud flows by means of high-resolution physical erosion modeling, high-resolution, radar-based precipitation data, and Unmanned Aerial Vehicle monitoring. Therefore, high-resolution, radar-based precipitation data products are used to assess the two storm events which triggered the mud flows in this unmonitored area. Subsequently, these data are used as meteorological input for the soil erosion model EROSION 3D to reconstruct and predict mud flows in the form of erosion risk maps. Finally, the model results are qualitatively validated by orthophotos generated from images from Unmanned Aerial Vehicle monitoring and Structure from Motion Photogrammetry. High-resolution, radar-based precipitation data reveal heavy to extreme storm events for both days. Erosion risk maps show erosion und deposition patterns and source areas as in reality, depending on the radar-based precipitation product. Consequently, reconstruction of the mud flows by these interdisciplinary methods is possible. Therefore, the development of an early warning system for soil erosion in agricultural landscapes by means of E 3D and high-resolution, radar-based precipitation forecasting data is certainly conceivable.

Ground Penetrating Radar (GPR) images are affected, to some degree, by the relative orientation of antennas and subsurface targets. This is particularly true not only for targets that show a significant directivity, but also for inclined planes, such as fractures and faults. Depending on the relative geometry between the antennas and the orientation of the target, radar waves can be preferentially scattered, which causes changes in the reflected signal amplitude. Therefore, traditional single polarization and single azimuth surveys may produce inadequate results. The work presented here examines the use of a multi-azimuth GPR survey to increase the imaging performance of inclined fractures, showing the shortcomings of single-profile surveying and highlighting the benefits that such a strategy has on detection and characterization.

Cassava (Manihot esculenta Crantz), a root crop utilized as food and industrial starch product, develops and maintains its marketable product sub-surface. Often, however, it is difficult to determine the potentially marketable goods available at any given time due to the sub-surface nature of the product and the inability to non-destructively sample. This dilemma has provided an avenue for application of ground penetrating radar. Relatively available designs of this technology, however, are cumbersome and do not provide the efficiencies for field applications. The objective of this research was to determine the functionality of a two Gigahertz frequency IDS GeoRadar C-Thrue antenna for the detection and parameterization of root architecture to be utilized for estimating marketable product. Cassava roots were buried across three horizontal and two vertical orientations to simulate the multi-directional nature of cassava roots. The antenna has dual polarization which also allowed to testing efficacy of polarization for detecting the varying root orientations. This study found that the C-Thrue system, more specifically, the Vertical transmit and Vertical receive polarization, was the most effective at accurately estimating cassava root length and widths at varying angles that simulate root development in true fields.

Subsurface imaging in arid regions is a well-known application of satellite Synthetic Aperture Radar (SAR). Archaeological prospection has often focused on L-band SAR sensors, given the ability of longer wavelengths to penetrate more deeply into sand. In contrast, this study demonstrates capabilities of shorter-wavelength, but higher spatial resolution, C-band and X-band SAR sensors in archaeological subsurface imaging at the site of ‘Uqdat al-Bakrah (Safah), Oman. Despite having varying parameters and acquisitions, both the X-band and C-band images analyzed were able to identify a subsurface paleo-channel that is not visible on the ground surface. This feature was first identified through Ground Penetrating Radar (GPR) survey, then recognized in the SAR imagery and further verified by test excavations. Both the GPR and the excavations reveal the base of the paleo-channel at a depth of 0.6 m–0.7 m. Hence, both X-band and C-band wavelengths are appropriate for subsurface archaeological prospection in suitable (dry silt and sand) conditions with specific acquisition parameters. Moreover, these results offer important new insights into the paleo-environmental context of ancient metal-working at ‘Uqdat al-Bakrah and demonstrate surface water flow roughly contemporary with the site’s occupation.

Radar rainfall forecasting is of major importance to predict flows in the sewer system to enhance early flood warning systems in urban areas. In this context, reducing radar rainfall estimation uncertainties can improve rainfall forecasts. This study utilises an ensemble generator that assesses radar rainfall uncertainties based on historical rain gauge data as ground truth. The ensemble generator is used to produce probabilistic radar rainfall forecasts (radar ensembles). The radar rainfall forecast ensembles are compared against a stochastic ensemble generator. The rainfall forecasts are used to predict sewer flows in a small urban area in the north of England using an Infoworks CS model. Uncertainties in radar rainfall forecasts are assessed using relative operating characteristic (ROC) curves, and the results showed that the radar ensembles overperform the stochastic ensemble generator in the first hour of the forecasts. The forecast predictability is however rapidly lost after 30 min lead-time. This implies that knowledge of the statistical properties of the radar rainfall errors can help to produce more meaningful radar rainfall forecast ensembles.

This paper focuses on high-resolution coastal stratigraphy data, which were revealed by the Ground Penetrating Radar (GPR) system. Surveys performed with GPR on the surface of prograded barriers reveal patterns of reflections that allow the interpretation of the geometry and stratigraphy of coastal deposits in a continuous mode. At the Curumim prograded barrier in southern Brazil (29°30′ S–49°53′ W), a two-dimensional transverse GPR survey revealed, with high precision, a serial of contacts between aeolian deposits of relict foredunes and relict beach deposits that have a strong correlation with sea level. In a 4 km GPR profile, a total of 24 of these contacts were identified. The high accurate spatial positioning of the contacts combined with Optical Stimulated Luminescence dating resulted in the first confident sea-level curve that tells the history of sea-level changes during the last 7 ka on the southernmost sector of the Brazilian coast. The curve shows that sea-level was still rising before 6 ka BP, with a maximum level of 1.9 m reached close to 5 ka BP; after that, sea-level started to falling slowly until around 4 ka BP when fall accelerated.

Ice thickness and bed topography of fast-flowing outlet glaciers are large sources of uncertainty for the current ice sheet models used to predict future contributions to sea-level rise. Due to a lack of coverage and difficulty in sounding and imaging with ice-penetrating radars, these regions remain poorly constrained in models. Increases in off-nadir scattering due to the highly crevassed surfaces, volumetric scattering (due to debris and/or pockets of liquid water), and signal attenuation (due to warmer ice near the bottom) are all impediments in detecting bed-echoes. A set of high-frequency (HF)/very high-frequency (VHF) radars operating at 14 MHz and 30–35 MHz were developed at the University of Kansas to sound temperate ice and outlet glaciers. We have deployed these radars on a small unmanned aircraft system (UAS) and a DHC-6 Twin Otter. For both installations, the system utilized a dipole antenna oriented in the cross-track direction, providing some performance advantages over other temperate ice sounders operating at lower frequencies. In this paper, we describe the platform-sensor systems, field operations, data-processing techniques, and preliminary results. We also compare our results with data from other ice-sounding radars that operate at frequencies both above (Center for Remote Sensing of Ice Sheets (CReSIS) Multichannel Coherent Depth Sounder (MCoRDS)) and below (Jet Propulsion Laboratory (JPL) Warm Ice Sounding Explorer (WISE)) our HF/VHF system. During field campaigns, both unmanned and manned platforms flew closely spaced parallel and repeat flight lines. We examine these data sets to determine image coherency between flight lines and discuss the feasibility of forming 2D synthetic apertures by using such a mission approach.

The paper investigates the superficial layers of an archaeological landscape based on the integration of various remote sensing techniques. It is well known in the literature that shallow depths may be rich in archeological remains, which generate different signal responses depending on the applied technique. In this study three main technologies are examined, namely ground-penetrating radar (GPR), ground spectroscopy, and multispectral satellite imagery. The study aims to propose a methodology to enhance optical remote sensing satellite images, intended for archaeological research, based on the integration of ground based and satellite datasets. For this task, a regression model between the ground spectroradiometer and GPR is established which is then projected to a high resolution sub-meter optical image. The overall methodology consists of nine steps. Beyond the acquirement of the in-situ measurements and their calibration (Steps 1–3), various regression models are examined for more than 70 different vegetation indices (Steps 4–5). The specific data analysis indicated that the red-edge position (REP) hyperspectral index was the most appropriate for developing a local fusion model between ground spectroscopy data and GPR datasets (Step 6), providing comparable results with the in situ GPR measurements (Step 7). Other vegetation indices, such as the normalized difference vegetation index (NDVI), have also been examined, providing significant correlation between the two datasets (R = 0.50). The model is then projected to a high-resolution image over the area of interest (Step 8). The proposed methodology was evaluated with a series of field data collected from the Vésztő-Mágor Tell in the eastern part of Hungary. The results were compared with in situ magnetic gradiometry measurements, indicating common interpretation results. The results were also compatible with the preliminary archaeological investigations of the area (Step 9). The overall outcomes document that fusion models between various types of remote sensing datasets frequently used to support archaeological research can further expand the current capabilities and applications for the detection of buried archaeological remains.

Karst geological anomalies at pile locations significantly affect the bearing capacity and construction safety of the piles, posing a significant challenge for urbanization. Borehole geophysical methods are required to extend the detection range and identify karst voids that are at pole locations and near drilled boreholes. In this paper, we developed a near offset and small diameter single borehole ground penetration radar (GPR) prototype. A signal processing method combining complex signal analysis by Hilbert transform (HT) and medium filtering was suggested to differentiate the weak backscattered wave from borehole background noise. A controlled horizontal borehole experiment was used to demonstrate the applicability of the prototype and the advantages of the signal analysis method prior to application in a real project. The controlled test presented three typical wave events corresponding to a soil–rock interface, rock fractures, and karst voids. Field tests were conducted at a freeway bridge extension project in an urban karst area. Multiple karst voids, sinkholes, rock fractures, and integrated bedrock were identified by analysis of four typical detection scenarios. The remediation of the karst voids and a rotary bored piling with real-time steel casing construction strategy were designed based on the investigation results. The construction feedback demonstrates that single borehole radar detection is effective for the investigation of anomalies at pile locations in urban karst areas.

This paper deals with the application of geophysical prospections to the study of the Church of Saint Cosma in Helerito inside the Monastery of Saints Cosma and Damiano (Tagliacozzo, L’Aquila, Italy). The research aimed to verify the presence of buried ancient archaeological structures of a construction phase older than the current building. Thus, a grid of ground-penetrating radar (GPR) profiles was carried out inside the building in the available spaces using a radar unit equipped with a multi-frequency antenna of 200–600 MHz. The analysis of 2D radargrams and horizontal slices relative to different temporal ranges led to the identification of significant regular patterns of the amplitude of the electromagnetic signals. The results suggest the presence of a buried structure below the analyzed surface.

Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides.