This study uses multi-temporal Copernicus Sentinel-2 imagery (2015-2024) to assess recent climate-driven changes in surface water bodies and marshlands in central and southern Iraq. Despite the temporal observation limited to the last decade, the results reveal important variations in the extent and morphology of water bodies and marshlands (e.g. 60 % decrease at Al Hammar marshland) that suggest worsening of the condition when the analysis is compared to the scenario observed in 1990-2000s. While some lakes, such as Razzaza, showed transient fluctuations, others, including the Al Dalmajh marsh and UNESCO listed wetlands, experienced persistent shrinking, indicating progressive drying. NDVI and NDWI indices highlight notable declines in vegetation and water coverage, reflecting growing ecological stress. The findings demonstrate the effectiveness of Sentinel-2 data for monitoring hydrological and environmental changes in arid regions, and underscore the importance of integrating other type of observations (such as Synthetic Aperture Radar, SAR) to overcome atmospheric constraints and enhance temporal continuity in future analyses.

Subsidence poses a significant geohazard to reclaimed land, necessitating continuous monitoring for effective management. Synthetic aperture radar (SAR) and interferometric SAR techniques have been widely used, though the full potential of multifrequency datasets remains underexplored. This study investigates subsidence in the Nakdong River Delta, South Korea, using multitemporal Persistent Scatterer Interferometry (PSI) applied to three SAR datasets: X-band COSMO-SkyMed, C-band Sentinel-1, andL-bandALOS-2PALSAR-2 StripMap and ScanSAR, acquired from 2015 to 2019. Data acquisitions were selected to ensure temporal overlap for meaningful comparison. Results show consistent subsidence patterns across four subregions-Busan New Port (BNP), Noksan Industrial Complex (NNIC), Hwajeon Industrial Complex (HIC), and Sinho Industrial Complex (SIC)-despite differences in SAR frequency and resolution. The highest subsidence rates were observed in SIC (-7.86 cm/year), BNP (-7.08 cm/year), and HIC (-6.40 cm/year), with NNIC showing the lowest rate (-4.97 cm/year). The comparative analysis demonstrates the complementary advantages of multifrequency SAR for subsidence monitoring in geotechnically complex environments. Without ground reference data, cross-validation among datasets was performed to enhance the reliability of the results. The findings emphasize the influence of sensor characteristics, phase stability, and coherence on PSI-based deformation estimates, highlighting the importance of careful parameter selection. This study provides insights into the strengths and limitations of different SAR frequencies, contributing to improved long-term monitoring strategies in dynamic reclamation zones.

Land subsidence is a global threat. In Europe, it impacts tens of millions of people by increasing flood risk and damage to the built environment. Land subsidence is the result of a combination of natural and human-induced subsurface processes, and there is considerable potential to mitigate the human-induced drivers through policy and legislation. We show how Europe is approaching the issues of measuring and addressing subsidence at a continental scale. We look ahead to the challenges of climate change and the energy transition in the context of amplifying the impacts of land subsidence, providing lessons learned from the European approach. Millions of people are impacted by land subsidence, which is accelerated by human-induced drivers. Across Europe, research and policy is focused on managing these drivers and mitigating their influence, with data and monitoring infrastructure playing a key role in tackling future challenges.

Iraq faces significant challenges in sustainable water resource management, due to intensive agriculture and climate change. Modern irrigation leads to depleted natural springs and abandoned traditional canal systems, creating a nexus between climate, water availability, agriculture, and cultural heritage. This work unveils this nexus holistically, from the regional to the local scale, and by considering all the components of the nexus. This is achieved by combining five decades (1974–2024) of satellite data—including declassified HEXAGON KH-9, Copernicus Sentinel-1/2/3, COSMO-SkyMed radar, and PlanetScope’s Dove optical imagery—and on-the-ground observations (photographic and drone surveying). The observed landscape changes are categorised as “proxies” to infer the presence of the given land processes that they correlate to. The whole of southern Iraq is afflicted by dust storms and intense evapotranspiration; new areas are desertifying and thus becoming local sources of dust in the southwest of the Euphrates floodplain and close to the boundary with the western desert. The most severe transformations happened around springs between Najaf Sea and Hammar Lake, where centre-pivot and herringbone irrigation systems fed by pumped groundwater have densified. While several instances of run-off and discharge highlight the loss of water in the western side of the study area, ~5 km2 wide clusters of crops in the eastern side suffer from water scarcity and are abandoned. Here, new industrial activities and modern infrastructure have already damaged tens of archaeological sites. Future monitoring based on the identified proxies could help to assess improvements or deterioration, in light of mitigation measures.

High to very high susceptibility and hazard levels of land subsidence have been identified in several Italian regions: Emilia-Romagna and Veneto regions, where loss of land elevation up to 7 cm/year in the Po River Plain impacts 30% of the Italian population since the 1950s; Puglia, e.g. at Tavoliere Plain, with land subsidence up to 2 cm/year; the Florence-Prato-Pistoia Plain in Tuscany and the Volturno Plain in Campania, with more 2 cm/year; the Gioia Tauro Plain in Calabria with more than 1 cm/year. Assessing the contribution of urbanization and the growth in urban population to this process is still a challenging task. The SubRISK+ project is aimed to provide new Earth observation-derived products and tools to improve the comprehension of current and future land subsidence in major urbanized areas of Italy. The project is funded by the European Union – Next Generation EU, in the framework of the Research Projects of Significant National Interest (PRIN) - National Recovery and Resilience Plan (PNRR) call 2022, and it is led by the National Research Council of Italy in Rome with the collaboration of the University School for Advanced Studies of Pavia and the University of Padua. In particular, the risk associated to land subsidence will be investigated for the 15 metropolitan cities of Italy and the Emilia Romagna region by exploiting Copernicus’ European Ground Motion Service (EGMS) data. Satellite-based Interferometric Synthetic Aperture Radar (InSAR) observations will be employed to map the current land subsidence and assess the potential induced damages to urban infrastructures. Then, a multidisciplinary approach incorporating geological, hydrogeological, geotechnical, land use data, and ground displacement observations will be implemented to disentangle the contribution of various processes and evaluate the associated triggers. The activities will be performed across national, regional, and local scales. The use of advanced groundwater flow and geomechanics model for a “hotspot city” case study will allow to quantify the effects of groundwater exploitation and estimate uncertainties in land subsidence. Market and non-market direct/indirect losses will be assessed at national, regional, and local scales via a newly developed socio-economic impact analysis, based on the exposure, vulnerability, and resilience of the investigated urbanized areas. Finally, future land subsidence risk scenarios will be estimated in the medium (2050) and long term (2100).

By altering aquifer storage capacity, groundwater level (GWL) plays a critical role in driving surface deformation, including ground subsidence and uplift. Groundwater depletion can induce sinkholes or subsidence, whereas recharge can cause surface uplift. These processes pose significant risks to soft grounds composed of soft alluvial sediments, emphasizing the importance of regular monitoring. In this study, we applied the small baseline subset (SBAS) technique to conduct a time-series analysis of surface deformation in Gimhae City, South Korea, where a continuous GWL increase was observed. Seasonal trend decomposition using the Loess (STL) method was employed to isolate the long-term GWL trend by removing seasonal variability. Multi-frequency synthetic aperture radar datasets, including ALOS PALSAR, COSMO-SkyMed, and Sentinel-1, revealed a cumulative surface uplift of approximately 9.2 cm, primarily concentrated along the deepest GWL contour line and confined between two lineament structures. The decomposed velocities from Sentinel-1 highlighted the predominance of vertical displacement over horizontal movement. Time-series analyses consistently showed uplift patterns, whereas correlation analysis demonstrated a strong relationship (R2 > 0.75) between surface deformation and GWL changes from 2013 to 2021. These results suggest a significant link between surface uplift and the rising GWL in Gimhae City, providing insights into the hydrogeological processes that influence ground deformation. Furthermore, a time lag between the GWL changes and surface displacement was identified, providing valuable insights into the dynamics of groundwater-related surface deformation.

Land subsidence affects many world metropolises, impacting their infrastructure and population. This work showcases an innovative methodology for exposure-vulnerability rating, hazard quantification and risk assessment that integrates remotely sensed information on ground displacement, land cover and settlement characteristics. Land subsidence-induced deformation and structural stress are quantified within the 15 metropolitan cities of Italy, along with the amount of residential/non-residential infrastructure and population exposed. A total of 1.44 out of 2665 km2 urbanised land within the 15 cities is at high risk due to significant angular distortions (and, sometimes, additive threat from horizontal strain) affecting very high exposure-vulnerability infrastructure; for more than 2700 buildings there is high likelihood of already occurred/incipient structural damage. This reference knowledge-base on present-day subsidence-induced risk can inform land and risk management at national scale, and provides a baseline for future assessments to build upon with a look to the next decades and sustainable urban development.

Rapid urbanization has transformed cityscapes worldwide, yet vertical urban growth (VUG) receives less attention than horizontal expansion. This study mapped and analyzed VUG patterns in Wuhan, China, from 2012 to 2020 based on a Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) dataset derived from a long time series of 375 COSMO-SkyMed SAR images. The methodology involved full-stack processing (analyzing all 375 images for a stable reference), sub-stack processing (independently processing sequential image subsets to track temporal changes), and post-processing to extract persistent scatterer (PS) candidates, estimate building heights, and analyze temporal changes. Validation was conducted through drone surveys and ground measurements in the Hanyang district. Results revealed substantial vertical expansion in central districts, with Hanyang experiencing a 66-fold increase in areas with buildings exceeding 90 m in height, while Hongshan district saw a 34-fold increase. Peripheral districts instead displayed more modest growth. Time series analysis and 3D visualization captured VUG temporal dynamics, identifying specific rapidly transforming urban sectors within Hanyang. Although the study is focused on one city with accuracy assessed on a spatially confined sample of more than 500 buildings, the findings suggest that PSInSAR height estimates from high-resolution SAR imagery can complement global settlement datasets (e.g., Global Human Settlement Layer, GHSL) in order to achieve better accuracy for individual building heights. Validation generally confirmed the accuracy of PSInSAR-derived height estimates, though challenges remain with noise and the distribution of PS. The location of PS along the building instead of the building rooftops can affect height estimation precision.

Change detection (CD) based on multitemporal remote sensing imagery is a crucial step for various Earth observation applications. While deep learning (DL) has revolutionized CD, its data-driven nature demands substantial labeled images for supervised model training, which is costly and time-consuming. This article addresses the challenge of limited training samples by proposing a novel object-based change augmentation (OCA) method. Unlike conventional image-level augmentation methods that can introduce irrelevant contextual dependencies, OCA decomposes the augmentation process into few-shot object classification and foreground-background pasting, thereby generating in-distribution synthetic images with increased change diversity. An object-based training strategy is developed to create a high-confidence binary classifier for pseudosemantic segmentation, facilitating the copy-paste operation. Experimental results on the very-high-resolution remote sensing images demonstrate the superior performance of OCA compared to existing augmentation- and generation-based methods. A comprehensive analysis of parameter sensitivity, adaptability to varying training data volumes, and compatibility with diverse CD methods validates its robustness. This approach provides a practical and effective solution for few-shot CD scenarios, advancing the applicability of DL-based CD methods in training data-limited environments. Codes and data are available: https://github.com/openrsgis/OCA

Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to exploit satellite differential interferometric synthetic aperture radar (DInSAR) data to investigate land subsidence processes, using European Ground Motion Service (EGMS) Sentinel-1 DInSAR 2018−2022 datasets. The workflow involves the implementation of Persistent Scatterers (PS) time series classification through the PS-Time tool, deformation signal decomposition via independent component analysis (ICA), and drivers’ investigation through spatio-temporal correlation with geospatial and monitoring data. Subsidence time series at the three demonstration sites of Bologna, Ravenna and Carpi (Po Plain, Italy) were classified into linear and nonlinear (quadratic, discontinuous, uncorrelated) categories, and the mixed deformation signal of each PS was decomposed into independent components, allowing the identification of new spatial clusters with linear, accelerating/decelerating, and seasonal trends. The relationship between the different independent components and DInSAR-derived displacement velocity, acceleration, and seasonality was also analyzed via regression analysis. Correlation with geological and groundwater monitoring data supported the investigation of the relationship between the observed deformation and subsidence drivers, such as aquifer resource exploitation, local geological setting, and gas extraction/reinjection.

Earth observation data and advanced modeling are used in this work to generate products and tools aiming to enhance the understanding of subsidence risk in major urban areas of Italy, towards sustainable use of groundwater resources and urban development. A multi-scale methodology is designed to assess present-day subsidence risk using satellite-based ground deformation observations, hydrogeological, topographic and land use data, and to identify subsidence hotspots and drivers using geostatistics. Advanced numerical modelling coupling 3D transient groundwater flow and geomechanics enables quantification of the effects of groundwater usage to land deformation. Future subsidence risk under climate change scenarios, demographic and urban development, is assessed by adapting the advanced model. Market and non-market direct/indirect losses are quantified via a bespoke socio-economic impact analysis.

Iraq is among the five countries most vulnerable to climate change impacts. Its cultural landscapes and heritage are exposed to erosion, weathering, abandonment and, eventually, disappearance. An approach combining satellite and on-the-ground observations to investigate anthropogenic and climate change-related processes is being developed in the framework of the Italian National Research Council – UK Royal Society bilateral cooperation programme. The data analysis workflow capitalises on decades of Earth observation imagery, including declassified HEXAGON, Copernicus Sentinel-1 radar, Sentinel-2 and Sentinel-3 multispectral data. The goal is to delineate archaeological sites and ancient water systems (canals, water bodies, wells), and generate derived products (e.g. yearly change detection maps based on interferometric coherence and vegetation indices variation, zonation of areas impacted by dust storms) depicting occurred transformations, regional susceptibility and endangered heritage sites.

This study investigates the 2018-2022 evolution of land subsidence in Ravenna, Italy, employing Sentinel-1 C-band Differential Interferometric Synthetic Aperture Radar (DInSAR) displacement time series (TS). Temporal deformation patterns were analyzed by applying an automated classification tool that exploits regression analyses and conditional sequences of statistical tests including ANOVA F, and its performance is evaluated. Mean displacement velocity, acceleration, seasonality and trend classification maps were created, revealing distinct TS behaviors and spatial clusters across the study area. Anthropogenic drivers, including groundwater withdrawal and gas resource management activities, likely act as contributors to the subsidence process. The results indicate subsidence velocities ranging from -5 to -37 mm/year, with notable accelerations in agricultural and industrial zones, and a deceleration process occurring in the southern coastal area. Correlation with geological and land cover layers underscored the role of sediment compaction and landscape changes.

Remote sensing has increasingly supported archaeological and cultural heritage applications over the past century, and satellite Synthetic Aperture Radar (SAR) has played a key role in advancing this application field. In this paper, two case studies from the wider Province of Rome (Italy) are exploited to investigate SAR imaging capabilities for archaeological prospection and heritage site protection. Medium to very high spatial resolution SAR data acquired by RADARSAT-2, Sentinel-1, ALOS-1 and COSMO-SkyMed are used to trial the detection of crop marks at (semi-)buried and sub-surface archaeological features in Ostia-Portus. Big data stacks of Sentinel-1 imagery are processed with the parallelized Small BAseline Subset (SBAS) Interferometric SAR (InSAR) method to monitor the stability of cultural heritage assets within the UNESCO World Heritage Site of Rome.

Over the past ten years, large amounts of original research data related to Earth system science have been made available at a rapidly increasing rate. Such growing data stock helps researchers understand the human-Earth system across different fields. A substantial amount of this data is published by geoscientists as open-access in authoritative journals. If the information stored in this literature is properly extracted, there is significant potential to build a domain knowledge base. However, this potential remains largely unfulfilled in geoscience, with one of the biggest obstacles being the lack of publicly available related corpora and baselines. To fill this gap, the Earth Science Data Corpus (ESDC), an academic text corpus of 600 abstracts, was built from the international journal Earth System Science Data (ESSD). To the best of our knowledge, ESDC is the first corpus with the needed detail to provide a professional training dataset for knowledge extraction and construction of domain-specific knowledge graphs from massive amounts of literature. The production process of ESDC incorporates both the contextual features of spatiotemporal entities and the linguistic characteristics of academic literature. Furthermore, annotation guidelines and procedures tailored for Earth science data are formulated to ensure reliability. ChatGPT with zero- and few-shot prompting, BARTNER generative, and W2NER discriminative models were trained on ESDC to evaluate the performance of the name entity recognition task and showed increasing performance metrics, with the highest achieved by BARTNER. Performance metrics for various entity types output by each model were also assessed. We utilized the trained BARTNER model to perform model inference on a larger unlabeled literature corpus, aiming to automatically extract a broader and richer set of entity information. Subsequently, the extracted entity information was mapped and associated with the Earth science data knowledge graph. Around this knowledge graph, this paper validates multiple downstream applications, including hot topic research analysis, scientometric analysis, and knowledge-enhanced large language model question-answering systems. These applications have demonstrated that the ESDC can provide scientists from different disciplines with information on Earth science data, help them better understand and obtain data, and promote further exploration in their respective professional fields.

Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely used for topographic, geological and natural resource investigations. However, most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors. This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets, to address cases when partial overlaps and/or temporal gaps exist. A new Power Exponential Knothe Model (PEKM) fits and fuses overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses any temporal gaps in the series. Taking the city of Wuhan (China) as experiment area, COSMO-SkyMed (2011-2015), TerraSAR-X (2015-2019) and Sentinel-1 (2019-2021) SAR datasets were fused to map long-term surface deformation over the last decade. An independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison. The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The correlation coefficient of the overall results is 0.78, which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve. The experimental results are consistent with existing studies of surface deformation at Wuhan, demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.

This study assesses subsidence-induced risk to urban infrastructure in three major Italian cities—Rome, Bologna, and Florence—by integrating satellite-based persistent scatterer interferometric synthetic aperture radar (PSInSAR) ground displacement data with urban vulnerability metrics into a novel risk assessment workflow, incorporating land use and population data from the Copernicus Land Monitoring Service (CLMS)—Urban Atlas. This analysis exploits ERS-1/2, ENVISAT, and COSMO-SkyMed PSInSAR datasets from the Italian Extraordinary Plan of Environmental Remote Sensing, plus Sentinel-1 datasets from CLMS—European Ground Motion Service (EGMS), and spans a 30-year period, thus capturing both historical and recent subsidence trends. Angular distortion is introduced as a critical parameter for assessing potential structural damage due to differential settlement, which helps to quantify subsidence-induced hazards more precisely. The results reveal variable subsidence hazard patterns across the three cities, with specific areas exhibiting significant differential ground deformation that poses risks to key infrastructure. A total of 36.15, 11.44, and 0.43 km2 of land at high to very high risk are identified in Rome, Bologna, and Florence, respectively. By integrating geospatial and vulnerability data at the building-block level, this study offers a more comprehensive understanding of subsidence-induced risk, potentially contributing to improved management and mitigation strategies in urban areas. This study contributes to the limited literature on embedding PSInSAR data into urban risk assessment workflows and provides a replicable framework for future applications in other urban areas.

One of the most promising applications of satellite data is providing users in charge of land and emergency management with information and data to support decision making for geohazard mapping, monitoring and early warning. In this work, we consider ground displacement data obtained via interferometric processing of satellite radar imagery, and we provide a novel post-processing approach based on a Functional Data Analysis paradigm capable of detecting precursors in displacement time series. The proposed approach appropriately accounts for the spatial and temporal dependencies of the data and does not require prior assumptions on the deformation trend. As an illustrative case, we apply the developed method to the identification of precursors to a mud volcano eruption in the Santa Barbara village in Sicily, southern Italy, showing the advantages of using a Functional Data Analysis framework for anticipating the warning signal. Indeed, the proposed approach is able to detect precursors of the paroxysmal event in the time series of the locations close to the eruption vent and provides a warning signal months before a scalar approach would. The method presented can potentially be applied to a wide range of geological events, thus representing a valuable and far-reaching monitoring tool.

A multi-scale methodology is designed to assess the baseline and future land subsidence risk scenarios in major urban areas of Italy. Ground deformation observations from multi-temporal satellite Interferometric Synthetic Aperture Radar (InSAR), hydrogeological, topographic and land use datasets are embedded into an innovative risk assessment workflow, and processed with advanced geostatistics to identify the main subsidence hotspots and drivers. Future subsidence risk is assessed accounting for various climate change scenarios, demographic and urban development. The results for the 15 metropolitan cities of Italy, and Emilia Romagna region showcase the potential of the developed methodology and its benefits to inform water resource management and decision making, towards sustainable use of groundwater resources and urban development.

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian "Valley of the Kings" (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves.