The mining and reclamation of opencast coal mines affect the soil volumetric water content (SVWC1). An accurate measurement of the SVWC is critical for land reclamation. However, traditional methods often damage the soil structure and are time-consuming. Thus, a rapid and non-destructive method is required to measure the SVWC in reclaimed mining areas. This study aimed to evaluate the feasibility and effectiveness of using ground penetrating radar (GPR) for estimating SVWC in reclaimed mining areas. We obtained GPR data and collected soil profile samples from the South Dump of the Antaibao opencast coal mine in Pinglu District, Shuozhou City, Shanxi Province. Random Hough transformation and inverse distance weighted interpolation were used to analyze the two-dimensional soil water layer thickness (SWLT) and SVWC in different soil layers and profiles. The radar estimated and the sampling measured value of SVWC were consistent with the soil depth. The Pearson correlation coefficient (r) between the radar estimated and the sampling measured values of SVWC was 0.850 in different soil layers, the lowest root mean squared error (RMSE) was 0.43%, and the lowest relative root mean square error (RRMSE) was 3.80%. The r was up to 0.959, the lowest RMSE was 0.58% to 0.90%, and the lowest RRMSE was 1.46% in different profiles. These results demonstrate the method's feasibility and effectiveness, enabling the precise non-destructive estimation of SVWC. The results provide valuable technical support for the efficient reclamation and restoration of mining areas.

Flash floods are highly destructive natural disasters, particularly in arid and semi-arid regions like Egypt, where data scarcity poses significant challenges for analysis. This study focuses on the Wadi Al-Barud basin in Egypt's Central Eastern Desert (CED), where a severe flash flood occurred on 26-27 October 2016. This flash flood event, characterized by moderate rainfall (16.4 mm/day) and a total volume of 8.85 x 106 m3, caused minor infrastructure damage, with 78.4% of the rainfall occurring within 6 h. A significant portion of floodwaters was stored in dam reservoirs, reducing downstream impacts. Multi-source data, including Landsat 8 OLI imagery, ALOS-PALSAR radar data, Global Precipitation Measurements-Integrated Multi-satellite Retrievals for Final Run (GPM-FR) precipitation data, geologic maps, field measurements, and Triangulated Irregular Networks (TINs), were integrated to analyze the flash flood event. The Soil Conservation Service Curve Number (SCS-CN) method integrated with several hydrologic models, including the Hydrologic Modelling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), and European Hydrological System Model (MIKE-SHE), was applied to evaluate flood forecasting, watershed management, and runoff estimation, with results cross-validated using TIN-derived DEMs, field measurements, and Landsat 8 imagery. The SCS-CN method proved effective, with percentage differences of 5.4% and 11.7% for reservoirs 1 and 3, respectively. High-resolution GPM-FR rainfall data and ALOS-derived soil texture mapping were particularly valuable for flash flood analysis in data-scarce regions. The study concluded that the existing protection plan is sufficient for 25- and 50-year return periods but inadequate for 100-year events, especially under climate change. Recommendations include constructing additional reservoirs (0.25 x 106 m3 and 1 x 106 m3) along Wadi Kahlah and Al-Barud Delta, reinforcing the Safaga-Qena highway, and building protective barriers to divert floodwaters. The methodology is applicable to similar flash flood events globally, and advancements in geomatics and datasets will enhance future flood prediction and management.

The existence of dispersive clay soils can cause serious erosion, void, and structural damage due to an imbalance of the electrochemical forces within the particles, which causes the soil particles to be repulsive instead of being attracted to each other. Dispersivity is observed in several highway embankments in Mississippi, and the embankments have eroded and developed voids over time. The current study investigated the root cause of the voids observed within the subgrade of the state highway 477 in Mississippi and evaluated the dispersivity of high cations-based soil. As part of an investigative initiative, a 2D Ground Penetration Radar (GPR) of the highway embankment road to make a 2D profile of the soil subsurface media was surveyed to reveal that potential hotspots were overlooked, leading to suspected soil dispersivity and subsequent issues. To assess the extent of visible voids and sinkholes, dispersive tests, including the Double Hydrometer Test (DHT), were conducted to evaluate the dispersivity of the clay soils. A series of boreholes were drilled along the roadway to collect the soil samples, determine their physical properties, and identify clay soil dispersity within the soil profile. Following the confirmation of dispersive soil existence through these tests, advanced analyses, such as Scanning Electron Microscope (SEM) to identify the microstructures and the ionic compositions of the soil particles and Toxicity Characteristic Leaching Procedure Tests (TCLPT) to assess the solubility of high concentrated elements in liquid, were performed to comprehend the root cause of the soil dispersion. Based on the results of the analysis, the GPR wave cannot pass through the subgrade, which mostly happens due to the presence of the charge within the soil. Based on SEM, DHT, and TCLP test results, the soil samples have high cations, including the presence of K + . Moreover, a similar distribution of the ionic compositions was observed among the majority of the soil samples; however, the percent of dispersion regarding clay soil particles varied.

Waterlogging is a significant concern in urban areas and can result in severe disruptions and damage and it's an urban problem. This study is conducted in Thoothukudi and Tamil Nadu, which are particularly sensitive to waterlogging because of their geographical and meteorological circumstances. Using synthetic aperture radar (SAR) images from 2015 to 2022, topographical analysis, land use/land cover (LULC) data, and geological insights, this research intends to identify and assess areas prone to water logging. The data source for this study comprises rainfall records from the Indian Meteorological Department (IMD), Sentinel-1 SAR imagery, Sentinel-2 multispectral images from the European Space Agency (ESA), and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM).Terrain analysis was undertaken using DEM to generate elevation, slope, and aspect maps, while SAR data were processed to extract water pixels, which included the extraction of water pixels from SAR data for each year and overlaying them. The overlaid image was correlated with topographic maps to identify the high-risk regions. Key places such as Muthayapuram, Milavittan, Bryant Nagar, and Thalamuthunagar were constantly highlighted as prone to floods. Additionally, the saltpan regions, defined by low-lying water table levels, endure continuous flooding, demonstrating the usefulness of combining SAR imaging with topographic analysis for urban water management. These findings provide useful insights for urban planners and policymakers, underlining the need for deliberate steps to reduce waterlogging, maintain public health, and minimize infrastructure damage, thus enabling sustainable development in Thoothukudi.

In this study, a methodology is proposed to use dual-polarimetric synthetic aperture radar (SAR) to identify the spatial distribution of soil liquefaction. The latter is a phenomenon that occurs in conjunction with seismic events of a magnitude generally higher than 5.5-6.0 and which affects loose sandy soils located below the water table level. The methodology consists of two steps: first the spatial distributions of soil liquefaction is estimated using a constant false alarm rate method applied to the SPAN metric, namely the total power associated with the measured polarimetric channels, which is ingested into a bitemporal approach to sort out dark areas not genuine. Second, the obtained masks are read in terms of the physical scattering mechanisms using a child parameter stemming from the eigendecomposition of the covariance matrix-namely the degree of polarization. The latter is evaluated using the coseismic scenes and contrasted with the preseismic one to have rough information on the time-variability of the scattering mechanisms occurred in the area affected by soil liquefaction. Finally, the obtained maps are qualitatively contrasted against state-of-the-art optical and interferometric SAR methodologies. Experimental results, obtained processing a time-series of ascending and descending Sentinel-1 SAR scenes acquired during the 2023 Turkiye-Syria earthquake, confirm the soundness of the proposed approach.

Reservoir landslides represent a significant geological hazard that jeopardizes the safety of reservoirs. Deformation monitoring and numerical simulation are essential methodologies for elucidating the evolutionary patterns of landslides. Nonetheless, the existing approaches exhibit limitations in revealing the potential deformation mechanism. Consequently, this study proposes an innovative strategy that incorporates interferometric synthetic aperture radar (InSAR) deformation characteristics alongside fluid-solid coupling stress analysis to investigate the deformation, focusing on the Shuizhuyuan landslide within the Three Gorges Reservoir area as a case study. Using temporary coherence point InSAR technology, significant motion units were identified, with a maximum deformation rate of -60 mm/yr. The complete deformation time series reveals three independent components of landslide movement and their trigger factors geometrically. Subsequently, the saturation permeability coefficient of the sliding mass in the seepage analysis is modified with the assistance of InSAR deformation. Then, we coupled the seepage analysis results to FLAC3D model for stress and strain analysis, and determined the seepage-induced progressive failure mechanism and the deformation mode of the Shuizhuyuan landslide, driven by reservoir water-level (RWL) drop. The numerical simulation results aid in interpreting the deformation mechanism of different spatial and temporal patterns of landslides from three aspects: hydrodynamic pressure from rainfall infiltration, groundwater hysteresis caused by RWL drop, and seepage forces from RWL rise. Furthermore, our findings reveal that the dynamic factor of safety (FOS) of landslide during the InSAR observation period is highly consistent with the periodic fluctuations of the RWL. However, there is also a small trend of overall decline in FOS that cannot be ignored.

Simulating synthetic aperture radar (SAR) images of crater terrain is a crucial technique for expanding SAR sample databases and facilitating the development of quantitative information extraction models for craters. However, existing simulation methods often overlook crucial factors, including the explosive depth effect in crater morphology modeling and the double-bounce scattering effect in electromagnetic scattering calculations. To overcome these limitations, this article introduces a novel approach to simulating SAR images of crater terrain. The approach incorporates crater formation theory to describe the relationship between various explosion parameters and craters. Moreover, it employs a hybrid ray-tracing approach that considers both surface and double-bounce scattering effects. Initially, crater morphology models are established for surface, shallow burial, and deep burial explosions. This involves incorporating the explosive depth parameter into crater morphology modeling through crater formation theory and quantitatively assessing soil movement influenced by the explosion. Subsequently, the ray-tracing algorithm and the advanced integral equation model are combined to accurately calculate electromagnetic scattering characteristics. Finally, simulated SAR images of the crater terrain are generated using the SAR echo fast time-frequency domain simulation algorithm and the chirp scaling imaging algorithm. The results obtained by simulating SAR images under different explosion parameters offer valuable insights into the effects of various explosion parameters on crater morphology. This research could contribute to the creation of comprehensive crater terrain datasets and support the application of SAR technology for damage assessment purposes.

The abrupt occurrence of the Zhongbao landslide is totally unexpected, resulting in the destruction of local infrastructure and river blockage. To review the deformation history of the Zhongbao landslide and prevent the threat of secondary disasters, the small baseline subsets (SBAS) technology is applied to process 59 synthetic aperture radar (SAR) images captured from Sentinel-1A satellite. Firstly, the time series deformation of the Zhongbao landslide along the radar line of sight (LOS) direction is calculated by SBAS technology. Then, the projection transformation is conducted to determine the slope displacement. Furthermore, the Hurst exponent of the surface deformation along the two directions is calculated to quantify the hidden deformation development trend and identify the unstable deformation areas. Given the suddenness of the Zhongbao landslide failure, the multi-temporal interferometric synthetic aperture radar (InSAR) technology is the ideal tool to obtain the surface deformation history without any monitoring equipment. The obtained deformation process indicates that the Zhongbao landslide is generally stable with slow creep deformation before failure. Moreover, the Hurst exponent distribution on the landslide surface in different time stages reveals more deformation evolution information of the Zhongbao landslide, with partially unstable areas detected before the failure. Two potential unstable areas after the Zhongbao landslide disaster are revealed by the Hurst exponent distribution and verified by the GNSS monitoring results and deformation mechanism discussion. The method combining SBASInSAR and Hurst exponent proposed in this study could help prevent and control secondary landslide disasters. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/ by/4.0/).

Despite its crucial role in flood defense for downstream regions, the catastrophic breach of the Kakhovka Dam on June 6, 2023, along the Dnipro River in Ukraine caused extensive flooding and damage both upstream and downstream. In addition, the subsequent significant drying up of the dam reservoir poses serious challenges, including hindered electricity generation, compromised flood control measures, and disrupted aquatic ecosystems. This study aims to address knowledge gaps related to the event by employing multi-temporal change detection of pre- and post-event Sentinel-1 synthetic aperture radar (SAR) imagery, analyzed using the Google Earth Engine (GEE) platform, to map flood extent and impacts. Furthermore, we assessed the impacts of dam breaches on soil organic carbon (SOC) sequestration potential in both the drying reservoir region upstream and the flooded areas downstream. The results estimated the total area of the flood extent to be approximately 379.41 km2, with an overall accuracy (OA) of 94% and a Kappa index (K) of 0.89. Quantitative analysis revealed that 81.15 km2 of urban areas, 82.59 km2 of agricultural lands, and 215.56 km2 of herbaceous wetlands were submerged by floodwaters. Both flooding and reservoir drawdown from dam collapses can significantly affect soil organic carbon (SOC) sequestration rates in affected soils. The quantification of post-disaster impacts underscores the pressing need for restoration practices and sustainable management efforts to lessen the environmental impacts and enhance the recovery of the affected regions.

Site-specific estimates of precipitation can be used to assess crop productivity and identify areas vulnerable to crop damages caused by extreme weather events such as droughts and floods. Spatial interpolation of precipitation such as Parameter-elevation Regressions on Independent Slopes Model (PRISM) has been used to estimate precipitation in an area of interest. However, the reliability of spatial interpolation is often affected by the availability of precipitation measurements from weather stations in a given region especially under complex terrain conditions. Here we propose an alternative approach for site-specific estimation of precipitation using both radar reflectivity data and topographic features. At first, radar reflectivity data are used as inputs to an artificial neural network (ANN) for estimation of precipitation. These radar precipitations at each grid cell are used to represent the observations at virtual weather stations for spatial interpolation using PRISM. Furthermore, the radar precipitations are compared with the observations at actual weather stations for their bias correction. This approach is referred to as PRISM and Radar Estimation for Precipitation (PREP). A case study was conducted in Jeollabuk-do, South Korea to compare the degree of agreement between PREP and PRISM. It was found that PREP had higher degree of agreement for the daily estimates of precipitation than PRISM in the given region with a complex terrain including coast and mountains. For example, the root mean square error (RMSE) of precipitation estimates for PREP was 22.1% less than that for PRISM in 2020. PREP also had greater value of the critical success index (CSI) than PRISM especially under heavy precipitation events, e.g.,>180 mm, and no rainfall conditions. These findings indicate that the PREP would improve the reliability of site-specific estimates of precipitation, which would facilitate decision-making in agriculture and early warning of extreme weather events.