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Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities is therefore critical in mitigating the risk posed by their failure. The purpose of slope monitoring is to detect precursory signs of stability issues, such as changes in the rate of displacement with which a slope is deforming. This information can then be used to predict the timing or probability of an imminent failure in order to provide an early warning. Most approaches to predicting slope failures, such as the inverse velocity method, focus on predicting the timing of a potential failure. However, such approaches are deterministic and require some subjective analysis of displacement monitoring data to generate reliable timing predictions. In this study, a more objective, probabilistic-learning algorithm is proposed to detect and characterise the risk of a slope failure, based on spectral analysis of serially correlated displacement time-series data. The algorithm is applied to satellite-based interferometric synthetic radar (InSAR) displacement time-series data to retrospectively analyse the risk of the 2019 Brumadinho tailings dam collapse in Brazil. Two potential risk milestones are identified and signs of a definitive but emergent risk (27 February 2018-26 August 2018) and imminent risk of collapse of the tailings dam (27 June 2018-24 December 2018) are detected by the algorithm as the empirical points of inflection and maximum on a risk trajectory, respectively. Importantly, this precursory indication of risk of failure is detected as early as at least five months prior to the dam collapse on 25 January 2019. The results of this study demonstrate that the combination of spectral methods and second order statistical properties of InSAR displacement time-series data can reveal signs of a transition into an unstable deformation regime, and that this algorithm can provide sufficient early-warning that could help mitigate catastrophic slope failures. [ABSTRACT FROM AUTHOR]

• ISBAS method enables comprehensive delineation of ground deformation over Tengiz. • Increased spatial coverage of measurements compared to most conventional techniques. • Maximum rate of subsidence over field is 15.7 mm/year during 2004–2009. • Significant increase in subsidence during 2016–2017, peaking at 79.3 mm/year. Changes in subsurface pore pressures and stresses due to the extraction of hydrocarbons often cause deformation over oil and gas fields. This can have significant consequences, including ground subsidence, induced seismicity and well failures. Geodynamic monitoring is an important requirement in recognising potential threats in sufficient time for remedial measures to be implemented. Differential interferometric synthetic aperture radar (DInSAR) is increasingly utilised for monitoring ground deformation over oil and gas reservoirs, achieving greater spatial coverage than traditional field-based surveying techniques. However, ground deformation over oil and gas fields can extend regionally into the surrounding rural landscape, where many conventional DInSAR techniques are of limited use due to the dynamic nature of the land cover. The Intermittent Small Baseline Subset (ISBAS) method is an advanced DInSAR technique, which considers the intermittent nature of coherence over dynamic land cover types to obtain markedly more ground motion measurements in non-urban regions. In this study, the ISBAS technique is used to delineate deformation over the super-giant Tengiz oil field in rural Kazakhstan. Analysis of ENVISAT data for 2004–2009 reveals a well-defined bowl subsiding with a maximum rate of −15.7 mm/year, corroborated by independent DInSAR studies and traditional levelling data. Subsequent application of ISBAS to Sentinel-1 data reveals significant evolution of deformation over the field in 2016–2017, with subsidence increasing dramatically to a maximum of -79.3 mm/year. The increased density of measurements obtained using the ISBAS technique enables accurate and comprehensive delineation and characterisation of ground deformation in this rural landscape, without the need for corner reflectors. This enhanced information could ultimately aid reservoir characterisation and management, and improve understanding of the risk posed by ground subsidence and fault reactivation. [ABSTRACT FROM AUTHOR]

Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil) or regional variations in mineralogy and chemical composition can result in the generation of unrealistic, generalised lithological maps that exhibit the “salt-and-pepper” artefact of spurious pixel classifications, as well as poorly defined contacts. In this study, an object-based image analysis (OBIA) approach to lithological mapping is evaluated with respect to its ability to overcome these issues by instead classifying groups of contiguous pixels (i.e., objects). Due to significant vegetation cover in the study area, the OBIA approach incorporates airborne multispectral and LiDAR data to indirectly map lithologies by exploiting associations with both topography and vegetation type. The resulting lithological maps were assessed both in terms of their thematic accuracy and ability to accurately delineate lithological contacts. The OBIA approach is found to be capable of generating maps with an overall accuracy of 73.5% through integrating spectral and topographic input variables. When compared to equivalent per-pixel classifications, the OBIA approach achieved thematic accuracy increases of up to 13.1%, whilst also reducing the “salt-and-pepper” artefact to produce more realistic maps. Furthermore, the OBIA approach was also generally capable of mapping lithological contacts more accurately. The importance of optimising the segmentation stage of the OBIA approach is also highlighted. Overall, this study clearly demonstrates the potential of OBIA for lithological mapping applications, particularly in significantly vegetated and heterogeneous terrain. [ABSTRACT FROM AUTHOR]

Vegetation cover can affect the lithological mapping capability of space- and airborne instruments because it obscures the spectral signatures of the underlying geological substrate. Despite being widely accepted as a hindrance, few studies have explicitly demonstrated the impact vegetation can have on remote lithological mapping. Accordingly, this study comprehensively elucidates the impact of vegetation on the lithological mapping capability of airborne multispectral data in the Troodos region, Cyprus. Synthetic spectral mixtures were first used to quantify the potential impact vegetation cover might have on spectral recognition and remote mapping of different rock types. The modeled effects of green grass were apparent in the spectra of low albedo lithologies for 30%-40% fractional cover, compared to just 20% for dry grass cover. Lichen was found to obscure the spectra for 30%-50% cover, depending on the spectral contrast between bare rock and lichen cover. The subsequent impact of vegetation on the remote mapping capability is elucidated by considering the outcomes of three airborne multispectral lithological classifications alongside the spectral mixing analysis and field observations. Vegetation abundance was found to be the primary control on the inability to classify large proportions of pixels in the imagery. Matched Filtering outperformed direct spectral matching algorithms owing to its ability to partially unmix pixel spectra with vegetation abundance above the modeled limits. This study highlights that despite the limited spectral sampling and resolution of the sensor and dense, ubiquitous vegetation cover, useful lithological information can be extracted using an appropriate algorithm. Furthermore, the findings of this case study provide a useful insight to the potential capabilities and challenges faced when utilizing comparable sensors (e.g., Landsat 8, Sentinel-2, WorldView-3) to map similar types of terrain. [ABSTRACT FROM AUTHOR]

Change in urban land use and impervious surface cover are valuable sources of information for determining the environmental impacts of urban development. However, our understanding of these impacts is limited due to the general lack of historical data beyond the last few decades. This study presents two methodologies for mapping and revealing long-term change in urban land use and imperviousness from topographic maps. Method 1 involves the generation of maps of fractional impervious surface for direct computation of catchment-level imperviousness. Method 2 generates maps of urban land use for subsequent computation of estimates of catchment imperviousness based on an urban extent index. Both methods are applied to estimate change in catchment imperviousness in a town in the South of England, at decadal intervals for the period 1960–2010. The performance of each method is assessed using contemporary reference data obtained from aerial photographs, with the results indicating that both methods are capable of providing good estimates of catchment imperviousness. Both methods reveal that peri-urban developments within the study area have undergone a significant expansion of impervious cover over the period 1960–2010, which is likely to have resulted in changes to the hydrological response of the previously rural areas. Overall, results of this study suggest that topographic maps provide a useful source for determining long-term change in imperviousness in the absence of suitable data, such as remotely sensed imagery. Potential applications of the two methods presented here include hydrological modelling, environmental investigations and urban planning. [ABSTRACT FROM AUTHOR]

Structural maps are traditionally produced by mapping features such as faults, folds, fabrics, fractures and joints in the field. However, large map areas and the spatially limited ground perspective of the field geologist can potentially increase the likelihood that not all structural features will be identified within a given area. The ability to recognise and map both local and regional structural features using high-resolution remote sensing data provides an opportunity to complement field-based mapping to help generate more comprehensive structural maps. Nonetheless, vegetation cover can adversely affect the extraction of structural information from remotely sensed data as it can mask the appearance of subtle spectral and geomorphological features that correspond to geological structures. This study investigates the utility of airborne Light Detection And Ranging (LiDAR) data and airborne multispectral imagery for detailed structural mapping in vegetated ophiolitic rocks and sedimentary cover of a section of the northern Troodos ophiolite, Cyprus. Visual enhancement techniques were applied to a 4-m airborne LiDAR digital terrain model and 4-m airborne multispectral imagery to assist the generation of structural lineament maps. Despite widespread vegetation cover, dykes and faults were recognisable as lineaments in both data sets, and the predominant strike trends of lineaments in all resulting maps were found to be in agreement with field-based structural data. Interestingly, prior to fieldwork, most lineaments were assumed to be faults, but were ground-verified as dykes instead, emphasising the importance of ground-truthing. Dyke and fault trends documented in this study define a pervasive structural fabric in the upper Troodos ophiolite that reflects the original sea-floor spreading history in the Larnaca graben. This structural fabric has not previously been observed in such detail and is likely to be continuous in adjacent regions under sedimentary cover. This information may be useful to future exploration efforts in the region focused on identification of structurally controlled mineral and groundwater resources. Overall, our case study highlights the efficacy of airborne LiDAR data and airborne multispectral imagery for extracting detailed and accurate structural information in hard-rock terrain to help complement field-based mapping. [ABSTRACT FROM AUTHOR]

Abstract: Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map – with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 – using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities. [Copyright &y& Elsevier]

Abstract: Traditional field-based lithological mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Consequently, rock units are often mapped at coarse-scales, resulting in lithological maps that have generalised contacts which in many cases are inaccurately located. Remote sensing data, such as aerial photographs and satellite imagery are commonly incorporated into geological mapping programmes to obtain geological information that is best revealed by overhead perspectives. However, spatial and spectral limitations of the imagery and dense vegetation cover can limit the utility of traditional remote sensing products. The advent of Airborne Light Detection And Ranging (LiDAR) as a remote sensing tool offers the potential to provide a novel solution to these problems because accurate and high-resolution topographic data can be acquired in either forested or non-forested terrain, allowing discrimination of individual rock types that typically have distinct topographic characteristics. This study assesses the efficacy of airborne LiDAR as a tool for detailed lithological mapping in the upper section of the Troodos ophiolite, Cyprus. Morphometric variables (including slope, curvature and surface roughness) were derived from a 4m digital terrain model in order to quantify the topographic characteristics of four principal lithologies found in the area. An artificial neural network (the Kohonen Self-Organizing Map) was then employed to classify the lithological units based upon these variables. The algorithm presented here was used to generate a detailed lithological map which defines lithological contacts much more accurately than the best existing geological map. In addition, a separate map of classification uncertainty highlights potential follow-up targets for ground-based verification. The results of this study demonstrate the significant potential of airborne LiDAR for lithological discrimination and rapid generation of detailed lithological maps, as a contribution to conventional geological mapping programmes. [Copyright &y& Elsevier]

Coastal sediment grain size is an important factor in determining coastal morphodynamics. In this study, we explore a novel approach for retrieving the median sediment grain size (D50) of gravel-dominated beaches using Synthetic Aperture Radar (SAR) spaceborne imagery. We assessed this by using thirty-six Sentinel-1 (C-band SAR) satellite images acquired in May and June 2022 and 2023, and three NovaSAR (S-band SAR) satellite images acquired in May and June 2022, for three different training sites and one test site across England (the UK). The results from the Sentinel-1 C-band data show strong positive correlations (R2 ≥ 0.75 ) between the D50 and the backscatter coefficients for 15/18 of the resultant models. The models were subsequently used to derive predictions of D50 for the test site, with the models which exhibited the strongest correlations resulting in Mean Absolute Errors (MAEs) in the range 2.26–5.47 mm. No correlation (R2 = 0.04) was found between the backscatter coefficients from the S-band NovaSAR data and D50. These results highlight the potential to derive near-real time estimates of coastal sediment grain size for gravel beaches to better inform coastal erosion and monitoring programs. [ABSTRACT FROM AUTHOR]

The launch of Copernicus, the largest Earth Observation program to date, is significant due to the regular, reliable and freely accessible data to support space-based geodetic monitoring of physical phenomena that can result in natural hazards. In this study, wide area interferometric synthetic aperture radar (InSAR) capability is demonstrated by processing 435 Copernicus Sentinel-1 C-Band SAR images (May 2015–May 2017) using the Intermittent Small Baseline Subset (ISBAS) method to produce a wide-area-map (WAM) covering 53,000 km2 of the Netherlands, Belgium and Germany. The ISBAS-WAM contains over 19 million measurements, achieving a ground coverage of 94%. The retrieval of measurements over soft surfaces (i.e. agricultural fields, forests and wetlands) was crucial due the dominance of non-urban land cover. A statistical analysis of the velocities reveals that intermittently coherent measurements in rural areas can provide reliable, additional deformation information with a very high degree of confidence (5σ), which spatially correlates to known deformation features associated with compressible soils, infrastructure settlement, peat oxidation, gas production, salt mining and underground and opencast mining. The spatial distribution of deformations concurs with independent data sources, such as previous persistent scatterer interferometry (PSI) deformation maps, models of subsidence and settlement susceptibility, and quantitatively with GPS measurements over the Groningen gas field. Remotely derived deformation products, with near complete spatial coverage, provide a powerful screening tool for mitigation and remediation of geological and geotechnical issues to help in the protection of assets, property and life. The ISBAS-WAM demonstrates that routine generation of such products on a continental scale is now theoretically achievable, given the establishment of the Copernicus programme and the development of state-of-the-art InSAR methods, such as ISBAS. • ISBAS InSAR was used to produce a national deformation map with 94% coverage. • Pixels in rural areas provided reliable measurements at the 5σ level. • Motions were attributed to six main phenomena. • National deformation maps can provide critical information on potential geohazards. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (1000–2500 nm) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis. Arabica and Robusta samples from several growing locations were scanned using a “push-broom” system. Hypercubes were segmented to select single beans, and average spectra were measured for each bean. Partial Least Squares regression was used to build quantitative prediction models on single beans (n = 320–350). The models exhibited good performance and acceptable prediction errors of ∼0.28% for moisture and ∼0.89% for lipids. This study represents the first time that HSI-based quantitative prediction models have been developed for coffee, and specifically green coffee beans. In addition, this is the first attempt to build such models using single intact coffee beans. The composition variability between beans was studied, and fat and moisture distribution were visualized within individual coffee beans. This rapid, non-destructive approach could have important applications for research laboratories, breeding programmes, and for rapid screening for industry. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) is a novel technology for the food sector that enables rapid non-contact analysis of food materials. HSI was applied for the first time to whole green coffee beans, at a single seed level, for quantitative prediction of sucrose, caffeine and trigonelline content. In addition, the intra-bean distribution of coffee constituents was analysed in Arabica and Robusta coffees on a large sample set from 12 countries, using a total of 260 samples. Individual green coffee beans were scanned by reflectance HSI (980–2500 nm) and then the concentration of sucrose, caffeine and trigonelline analysed with a reference method (HPLC-MS). Quantitative prediction models were subsequently built using Partial Least Squares (PLS) regression. Large variations in sucrose, caffeine and trigonelline were found between different species and origin, but also within beans from the same batch. It was shown that estimation of sucrose content is possible for screening purposes (R 2 = 0.65; prediction error of ~ 0.7% w/w coffee, with observed range of ~ 6.5%), while the performance of the PLS model was better for caffeine and trigonelline prediction (R 2 = 0.85 and R 2 = 0.82, respectively; prediction errors of 0.2 and 0.1%, on a range of 2.3 and 1.1% w/w coffee, respectively). The prediction error is acceptable mainly for laboratory applications, with the potential application to breeding programmes and for screening purposes for the food industry. The spatial distribution of coffee constituents was also successfully visualised for single beans and this enabled mapping of the analytes across the bean structure at single pixel level. [ABSTRACT FROM AUTHOR]

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research. [ABSTRACT FROM AUTHOR]

Advances in differential interferometric synthetic aperture radar (DInSAR) processing algorithms, such as the Intermittent Small Baseline Subset (ISBAS), and increased data availability from SAR systems, such as Sentinel-1, provide the opportunity to increase the spatial and temporal density of ground deformation measurements. Such measurements, when combined with modelling, have the potential to make a significant cost-effective contribution to the progressive abandonment strategy of recently closed coalfields. Applications of DInSAR over coalfields have observed heave in coal measures rocks and temporal correlations between the rise of mine water and deformation time-series. The cessation of systematic dewatering can have a variety of detrimental impacts and knowledge of the time-scales (i.e. the rate of rebound) and structure of the mine system are crucial to the remediation strategy. Although mine plans and borehole measurements provide vital information in this regard, mine plans are often incomplete or inaccurate, whereas monitoring boreholes are spatially sparse. Consequently, groundwater can flow in unanticipated directions via goaf, mine shafts and roadways, making it difficult to determine where the impacts of rebound are likely to occur. In this study, ground deformation data obtained using ISBAS DInSAR on ENVISAT (2002–2009) and Sentinel-1 (2015–2019) data are used to develop a simple method to model groundwater rebound in abandoned coalfields. A forward analytical model based upon the principle of effective stress and mine water ponds is first implemented to estimate surface heave in response to changes in groundwater levels measured in monitoring boreholes. The forward model is then calibrated and validated using the ground deformation data. The DInSAR data were subsequently inverted to map the change in groundwater levels in greater detail across the coalfield and forecast surface discharges in order to support mitigation strategies. • Groundwater rebound in the Nottinghamshire coalfield, UK is investigated. • A forward analytical model relates changes in groundwater to surface deformation. • The model is calibrated and validated using ISBAS DInSAR on ENVISAT and Sentinel-1 data. • The DInSAR data is inverted to map changes in groundwater levels in greater detail. • The inverse map is used to forecast surface discharges to aid management strategies. [ABSTRACT FROM AUTHOR]

Abstract Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection. Highlights • Semi-automatic, Object-Based Image Analysis for geological lineament detection • Two methods derived using oppositely directed segmentation techniques • Integration of multi-sourced datasets creating a robust composite lineament network • Uses the Tilt Derivative transform to standardise datasets prior to integration • Combines airborne magnetic, radiometric and LiDAR datasets over SW England [ABSTRACT FROM AUTHOR]

Geochemical maps provide invaluable evidence to guide decisions on issues of mineral exploration, agriculture, and environmental health. However, the high cost of chemical analysis means that the ground sampling density will always be limited. Traditionally, geochemical maps have been produced through the interpolation of measured element concentrations between sample sites using models based on the spatial autocorrelation of data (e.g. semivariogram models for ordinary kriging). In their simplest form such models fail to consider potentially useful auxiliary information about the region and the accuracy of the maps may suffer as a result. In contrast, this study uses quantile regression forests (an elaboration of random forest) to investigate the potential of high resolution auxiliary information alone to support the generation of accurate and interpretable geochemical maps. This paper presents a summary of the performance of quantile regression forests in predicting element concentrations, loss on ignition and pH in the soils of south west England using high resolution remote sensing and geophysical survey data. Through stratified 10-fold cross validation we find the accuracy of quantile regression forests in predicting soil geochemistry in south west England to be a general improvement over that offered by ordinary kriging. Concentrations of immobile elements whose distributions are most tightly controlled by bedrock lithology are predicted with the greatest accuracy (e.g. Al with a cross-validated R 2 of 0.79), while concentrations of more mobile elements prove harder to predict. In addition to providing a high level of prediction accuracy, models built on high resolution auxiliary variables allow for informative, process based, interpretations to be made. In conclusion, this study has highlighted the ability to map and understand the surface environment with greater accuracy and detail than previously possible by combining information from multiple datasets. As the quality and coverage of remote sensing and geophysical surveys continue to improve, machine learning methods will provide a means to interpret the otherwise-uninterpretable. [ABSTRACT FROM AUTHOR]

Highlights: [•] Backcasting storm runoff response using novel method for generating historical LUC. [•] Increased peak flow and reduced response time following peri-urban development. [•] Attribution of flashy response using impervious cover plus storm drainage. [•] Increased impervious cover has greater impact on rural peak flows than urban. [•] Drainage more important determinant of storm response than land use type. [Copyright &y& Elsevier]

Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in excess pressure on groundwater resources. Thus, monitoring groundwater-induced land movement in both these cities is very important in terms of understanding the risk posed to assets. Here, Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology are used to study land movement for London and National Capital Territory (NCT)-Delhi from October 2016 to December 2020. The land movement velocities were found to vary between −24 and +24 mm/year for London and between −18 and +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels, and spatio-temporal variation of groundwater and land movement was studied in conjunction. It was broadly observed that the extraction of a large quantity of groundwater leads to land subsidence, whereas groundwater recharge leads to uplift. A mathematical model was used to quantify land subsidence/uplift which occurred due to groundwater depletion/rebound. This is the first study that compares C-band PSInSAR-derived land subsidence response to observed groundwater change for London and NCT-Delhi during this time-period. The results of this study could be helpful to examine the potential implications of ground-level movement on the resource management, safety, and economics of both these cities. [ABSTRACT FROM AUTHOR]