Earth, Volume 6, Issue 3 (September 2025) – 20 articles

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

Urbanization is causing a decrease in agricultural land. This leads to changes in cropping patterns, irrigation water availability, and water allowance. Therefore, change in cropping pattern, irrigation water availability, and water allowance were investigated in the Multan region of Pakistan using remote sensing and GIS techniques. The multi-temporal Landsat images with 30 m resolution were acquired for both Rabi (winter) and Kharif (summer) seasons for the years of 1988, 1999 and 2020. The image processing tasks including layer stacking, sub-setting, land use/land cover (LULC) classification, and accuracy assessment were performed using ERDAS Imagine (2015) software. The LULC maps showed a considerable shift of orchard area to urban settlements and other crops. About 82% of orchard areas have shifted to urban settlements and other crops from 1988 to 2020. The LULC maps for Kharif season indicated that cropped areas for cotton have decreased by 42.5% and the cropped areas for rice have increased by 718% in the last 32 years (1988–2020). During the rabi season, the cropped areas for wheat (Triticum aestivum L.) have increased by 27% from 1988 to 2020. The irrigation water availability and water allowance have increased up to 125 and 110% due to decrease in agricultural land, respectively. The overall average accuracies were found as 87 and 89% for Rabi and Kharif crops, respectively. The LULC mapping technique may be used to develop a decision support system for evaluating the changes in cropping pattern and their impacts on net water availability and water allowances. Full article

In recent years, remote sensing and geographic information systems (GISs) have become essential tools for effective landscape management. This study utilizes these technologies to analyze land use and land cover (LULC) changes in Dibru-Saikhowa National Park, a riverine ecosystem in Assam, India, from its designation as a national park in 2000 through 2024. The satellite imagery was used to classify LULC types and track landscape changes over time. In 2000, grasslands were the dominant land cover (28.78%), followed by semi-evergreen forests (25.58%). By 2013, shrubland became the most prominent class (81.31 km²), and degraded forest expanded to 75.56 km². During this period, substantial areas of grassland (29.94 km²), degraded forest (10.87 km²), semi-evergreen forest (12.33 km²), and bareland (10.50 km²) were converted to shrubland. In 2024, degraded forest further increased, covering 80.52 km² (23.47%). This change resulted since numerous areas of shrubland (11.46 km²) and semi-evergreen forest (27.48 km²) were converted into degraded forest. Furthermore, significant shifts were observed in grassland, shrubland, and degraded forest, indicating a substantial and consistent decline in grassland. These changes are largely attributed to recurring Brahmaputra River floods and increasing anthropogenic pressures. This study recommends a targeted Grassland Recovery Project, control of invasive species, improved surveillance, increased staffing, and the relocation of forest villages to reduce human impact and support community-based conservation efforts. Hence, protecting the landscape through informed LULC-based management can help maintain critical habitat patches, mitigate anthropogenic degradation, and enhance the survival prospects of native floral and faunal assemblages in DSNP. Full article

In recent decades, the River Ganges in India has been heavily contaminated with domestic waste and industrial toxins because of cultural activities, a lack of community awareness, an absence of sewage disposal facilities, and rapid population growth. Previous studies have focused separately on either the physical or social factors associated with River Ganges pollution but have not combined these elements in a single study. To fill this research gap, our study assesses the bio-physical and social vulnerability of the River Ganges by using a holistic approach. The following four sampling stations were selected: Rishikesh, Haridwar, Kanpur, and Varanasi. These locations were chosen to test the water quality in bio-physical aspects and to assess the social perceptions of river vulnerability among the residents and visitors. Perceptions of river water quality and likely sources of pollution were gathered via the distribution of over 1000 questionnaires. Data collection took place in the winter and summer of 2022 and 2023. The results showed that river water quality is not suitable for drinking purposes at any of the four cities without conventional treatment, and that the river is unsuitable for bathing at all locations, except upstream of Rishikesh. Nearly 50% of those questioned agreed that the river is polluted, whilst 74% agreed that pollution has increased in recent decades, particularly in the last 10 years. These compelling results are critical for policymakers and decision makers. They highlight the urgent need for novel strategies that address Ganges pollution while fostering community health education and environmental management. By dispelling myths surrounding river quality, this study strengthens the ongoing efforts to restore the Ganges, ensuring that it remains a vital lifeline for present and future generations. Full article

Community-based systems present a key option for water services, especially in rural areas. Our goal is to achieve a state-of-the-art understanding of joint rural water supply development in Finland over 150 years. A mixed-methods approach was used: a literature survey and a questionnaire to selected experts. Based on the literature, a table including 23 decisions considered the most influential strategic events from 1872 to 2022 was produced. The table was sent to 10 selected experts known to be deeply familiar with the theme, all of whom replied. Joint rural water services in Finland have evolved based on demand through co-operative principles. The first documented scheme was constructed in 1872, while governmental financial support to rural water services started in 1951. It expanded in various forms until it dramatically declined in recent years. Multi-locality may increase the need for these services in the future. The expert survey revealed the following most influential long-term decisions: the first official water co-operative established in 1907, the land reform for immigrants and war veterans introduced in 1945, the Committee for Rationalisation of Households established in 1950, the start of domestic manufacturing of plastic pipes in 1954, and the Water Act enacted in 1962 to start water pollution control. This paper reminds us that urban and rural services are not contradictory but can supplement each other. Full article

This study explores the use of ensemble machine learning models to develop wildfire susceptibility maps (WFSMs) in Greece, focusing on their application as regressors. We provide a continuous assessment of wildfire risk, enhancing the interpretability and accuracy of predictions. Two key metrics were developed: Ensemble Mean and Ensemble Max. This dual-metric approach improves predictive robustness and provides critical insights for wildfire management strategies. The ensemble mode effectively handles complex, high-dimensional data, addressing challenges such as over fitting and data heterogeneity. Utilizing advanced techniques like XGBoost, GBM, LightGBM, and CatBoost regressors, our research demonstrates the potential of these methods to enhance wildfire risk estimation. The Ensemble Mean model classified 50% of the land as low risk and 21% as high risk, while the Ensemble Max model identified 38% as low risk and 33% as high risk. Notably, 83% of wildfires between 2000 and 2024 occurred in areas marked as high-risk by both models. The findings reveal that a significant proportion of wildfires occurred in areas identified as high risk by both ensemble models, underscoring their effectiveness. This approach offers significant potential to mitigate wildfires’ environmental, economic, and social impacts, enhance climate resilience, and strengthen preparedness for future wildfire events. Full article

Increasing global population and threat from climate change are imposing economic, social, and ecological challenges to global food production. The demand for food is increasing, necessitating enhanced agricultural production with minimal environmental impacts. To meet this demand, sustainable intensification of both crops and livestock is necessary. This is more urgent in sub-Saharan Africa (SSA), a region characterized by low productivity and environmentally degrading agriculture. Integrated Agriculture-livestock-forestry (ALF) systems could be a key form of intensification needed for achieving food security and economic and environmental sustainability. The synergetic interactions between ALF nexus provide a mechanism to foster interconnectedness and resource circulation where practices of one system influence the outcomes in another. These systems enhance long-term farm sustainability while serving the farmers’ environmental and economic goals. It provides opportunities for improving food security, farmer incomes, soil health, climate resilience and the achievement of several UN Sustainable Development Goals. It is therefore crucial to strengthen the evidence supporting the contribution of these systems. On this basis, this paper reviews the potential pathways through which ALF nexus can enhance incomes, food security and climate change mitigation in SSA. The paper discusses the pathways through which the integration of crops, livestock and trees enhance (i) food security, (ii) incomes, (iii) soil health and (iv) mitigation of climate change in SSA. We argue that implementing ALF systems will be accompanied by an advancement of enhanced food security, farmer livelihoods and ecological conservation. It will foster a more balanced and sustainable sub-Saharan African agricultural systems. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

This study analyzes the evolution of hydrometeorological risks and severe weather events in Catalonia through an extensive review of 21,312 news reports aired by Televisió de Catalunya (TVC) between 1984 and 2019, 10,686 (50.1%) of which focused on events within Catalonia. The reports are categorized by the type of phenomenon, geographic location, and reported impact, enabling the identification of temporal trends. The results indicate a general increase in the frequency of news coverage of hydrometeorological and severe weather events—particularly floods and heavy rainfall—both in Catalonia and the broader Mediterranean region. This rise is attributed not only to a potential increase in such events, but also to the expansion and evolution of media coverage over time. In the Catalan context, the most frequently reported hazards are snowfalls and cold waves (3203 reports), followed by rainfall and flooding (3065), agrometeorological risks (2589), and wind or sea storms (1456). The study highlights that rainfall and flooding pose the most significant risks in Catalonia, as they account for the majority of the reports involving serious impacts—1273 cases of material damage and 150 involving fatalities. The normalized data reveal a growing proportion of reports on violent weather and floods, and a relative decline in snow-related events. Full article

Glaciers are significant sources of fresh water on planet Earth. The Hindukush–Karakoram–Himalayan (HKH) glaciers provide the water supply to more than half of the human population of the globe, for agricultural activities, biodiversity survival, and ecosystem services. In recent years, the loss of glacial ice has been forecasted to cause problems such as sea level rise, changes in water availability, and release of contaminants that reside in the surfaces of glaciers or within them. In this regard, mineralogical sediments play a significant role in the geochemistry of glaciers and element cycling. This study analyzed elemental pollutants found in the glaciers of Pakistan and investigated the diverse bacterial communities residing therein. Samples of ice and sediments were collected from the Gilgit, Hunza, and Swat glaciers in northern Pakistan. Nine elements, including co-factors, heavy metals, and nutrients, were assessed using atomic absorption spectrophotometry. The research findings indicate higher concentrations of the elements K, Fe, Cu, and Cr in Hunza glacier ice (Hgi) and Ni, Zn, As, and Cd in Gilgit glacier ice (Ggi). In terms of glacier sediments, Swat (Sgs), Gilgit (Ggs), and Hunza (Hgs) samples showed the highest concentrations of K, Cu, Ni, Zn, As, Pb, Cd, and, respectively, of Fe, and Cr. The amount of Cu and Cr is the same in Swat glacier ice and Swat glacier foot. However, the concentration of some elements (As, K, Pb, Zn) is higher in Swat glacier ice, while the amount of some elements (Cd, Ni) is greater in Swat glacier foot. Furthermore, microbial cultivation techniques revealed diverse bacterial communities inhabiting the sampled glaciers. Phylogenetic analysis of the bacterial isolates, based on 16S rRNA gene sequences, showed high homology (99–100%) with previously reported species. The resultant phylogenetic tree grouped the bacterial isolates, such as Serratia marcescens, Cupriavidus sp., and Bacillus cereus, with closely related species known for their roles in nutrient cycling, environmental resilience, and metal tolerance. These findings highlight the ecological significance and adaptive potential of microbial communities in glacier environments, emphasizing their role in elemental cycling and environmental resilience. Full article

Danube River represents a critical axis of ecological and economic importance for the countries along its course. From this perspective, this paper aims to assess the most significant characteristics of the river and of its main tributaries, as well as its impact on the environmental sustainability and socio-economic development. Navigation and the economic contribution of the Danube River are the key issues of this work, emphasizing its importance as an international transport artery that facilitates trade and tourism, and develops the energy industry through hydropower plants. The study includes an analysis of the volume of goods transported from 2019 to 2023, as well as an analysis of the goods traffic in the busiest port on the Danube. Furthermore, climate change affects the hydrological regime of the Danube, as well as the ecosystems, economy, and energy security of the riparian countries. Main impacts include changes in the hydrological regime, increased frequency of droughts and floods, reduced water quality, deterioration of biodiversity, and disruption of the economic activities dependent on the river, such as navigation, agriculture, and hydropower production. Thus, hydrological risks and challenges are investigated, focusing on the extreme events of the last two decades and the awareness of their repercussions. In this context, the national and international institutions responsible for monitoring and managing the Danube are presented, and their role in promoting a sustainable river policy is explored. Methods and technologies are shown to be essential tools for monitoring and prediction studies. The Danube includes an extensive network of hydrometric stations that help to prevent and manage the most significant risks. Finally, a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of the development of the hydrological studies was conducted, highlighting the potential of the river. Full article

Flooding poses significant challenges in semi-arid regions, where irregular rainfall patterns increase environmental vulnerability. This study explicitly aims to improve flood susceptibility mapping by integrating advanced machine learning (ML) algorithms with geographic information systems (GIS) and remote-sensing data. Using data from the Upper Drâa Basin in southern Morocco, we applied boosting algorithms, including XGBoost, CatBoost, LightGBM, and Hist Gradient Boosting, to enhance the accuracy of flood risk assessment. Quantitative model evaluation shows that Hist Gradient Boosting achieved the best performance, with the lowest mean squared error (MSE = 0.06897) and root mean squared error (RMSE = 0.2626). It also attained the highest F1 score (0.8), overall accuracy (93.1%), and area under the curve (AUC = 0.833), indicating its superior predictive capability. These findings highlight the strong potential of novel boosting ensemble learning methods in flood susceptibility prediction and contribute valuable, data-driven insights for policymakers and urban planners to support effective flood mitigation strategies in southern Morocco. Full article

This study aimed to evaluate the performance and robustness of the GR2m “Génie Rural à 2 paramètres au pas du temps Mensuel” rainfall–runoff model for simulating streamflow under past and future hydrometeorological shifts in the Medjerda, a data-scarce Mediterranean catchment in northern Tunisia characterized by limited hydrometeorological records and high climate variability. The evaluation was conducted across three subcatchments characterized by contrasting climatic conditions and representing the hydrometeorological pattern of the Medjerda catchment. To assess the model’s robustness, a calibration–validation process was applied. This method alternated between dry and wet periods and evaluated model performance through various criteria. Subsequently, GR2m was adopted to simulate projected discharge, using projections from the “Model for Interdisciplinary Research on Climate 5” (MIROC5) under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Standardized climate indices (SCIs) were employed to assess climate change impacts. The results demonstrate that GR2m performs well in simulating streamflow across different climatic conditions within the Medjerda catchment and maintains satisfactory performance when calibrated over a non-stationary climate period. The findings indicate a continuous decline in projected runoff and suggest a significant increase in extreme drought events. Full article

Artisanal small-scale gold mining (ASGM) is the largest source of global mercury (Hg) emissions. This study investigated Hg contamination in water, soil, sediment, fish, and cassava plants around ASGM sites in Gunung Pongkor, West Java, Indonesia. Hg concentration ranged from 0.06 to 4.49 µg/L in water; 0.420 to 144 mg/kg dw in soil; 0.920 to 150 mg/kg dw in sediment; 0.259 to 1.23 mg/kg dw in fish; 0.097 to 5.09 mg/kg dw in cassava root; and 0.350 to 8.84 mg/kg dw in cassava leaf. Geo-accumulation index (Igeo) analysis revealed moderate to heavy soil contamination upstream, likely due to direct ASGM input. In contrast, sediment Igeo values indicated heavy contamination downstream, suggesting Hg transport and sedimentation. Bioconcentration factors (BCFs) in fish were predominantly high in downstream and midstream areas, indicating enhanced Hg bioavailability. Bioaccumulation factors (BAFs) in cassava were higher in upstream areas. Health risk assessment, based on the Hazard Quotient (HQ) and Hazard Index (HI), identified ingestion as the primary exposure route, with children exhibiting significantly higher risks than adults. These findings highlight the significant Hg contamination associated with ASGM in Gunung Pongkor and emphasize the need for targeted mitigation strategies to protect human and environmental health. Full article

This study presents the first comprehensive nationwide assessment of mangrove ecosystems in the Maldives. Surveys were conducted across 162 islands in 20 administrative atolls, integrating field data, the literature, and secondary sources to map mangrove distribution, confirm species presence, and classify habitat types. Twelve true mangrove species were identified, with Bruguiera cylindrica, Rhizophora mucronata, and Lumnitzera racemosa emerging as dominant. Species diversity was evaluated using Shannon (H′), Margalef (d′), Pielou’s evenness (J′), and Simpson’s dominance (λ′) indices. Atolls within the northern and southern regions, particularly Laamu, Noonu, and Shaviyani, exhibited the highest diversity and evenness, while central atolls such as Ari and Faafu supported mono-specific or degraded stands. Mangrove habitats were classified into four geomorphological types: marsh based, pond based, embayment, and fringing systems. Field sampling was conducted using standardized belt transects and quadrats, with species verified using photographic documentation and expert validation. Species distributions showed strong habitat associations, with B. cylindrica dominant in marshes, R. mucronata and B. gymnorrhiza in ponds, and Ceriops tagal and L. racemosa in embayments. Rare species like Bruguiera hainesii and Heritiera littoralis were confined to stable hydrological niches. This study establishes a critical, island-level baseline for mangrove conservation and ecosystem-based planning in the Maldives, providing a reference point for tracking future responses to climate change, sea-level rise, and hydrological disturbances, emphasizing the need for habitat-specific strategies to protect biodiversity. Full article

Sustainable development is increasingly challenged by the growing threats of climate change. There is a close relationship between climate change, public health, and Sustainable Development Goals (SDGs). This study investigates the temperature anomalies in the Kingdom of Bahrain and their potential effects on human health. Furthermore, it proposes solutions to support Bahrain’s SDG-related goals. Data were collected from global studies and statistics and the Bahrain Meteorological Directorate over 50 years, which were then used to calculate the temperature anomalies and the heat indices, thereby exploring the past and present monthly and annual national temperature and sociated risks to human health. The results show that Bahrain is located in an area of high temperature anomalies and high rates of cardiovascular diseases. Furthermore, anomaly calculations indicate a critical rise in temperature, ranging from 1 to 4 °C higher than the averages recorded in the 1960s, 1970s, and 1980s. Such an increase could significantly affect human health, particularly since the heat index results show that summers consistently fall within the extreme danger ranges. In contrast, other seasons have occasionally reached the danger level or required extreme caution in certain years. Consequently, this study offers recommendations to help mitigate the rise in temperature and associated risks in the future. Full article

Machine learning models are increasingly used for streamflow prediction due to their promising performance. However, their data-driven nature makes interpretation challenging. This study explores the interpretability of a Random Forest model trained on high streamflow events from a hydrological perspective, comparing methods for assessing feature influence. The results show that the mean decrease accuracy, mean decrease impurity, Shapley additive explanations, and Tornado methods identify similar key features, though Tornado presents the most notable discrepancies. Despite the model being trained with events of considerable temporal variability, the last observed streamflow is the most relevant feature accounting for over 20% of importance. Moreover, the results suggest that the model identifies a catchment region with a runoff that significantly affects the outlet flow. Accumulated local effects and partial dependence plots may represent first infiltration losses and soil saturation before precipitation sharply impacts streamflow. However, only accumulated local effects depict the influence of the scarce highest accumulated precipitation on the streamflow. Shapley additive explanations are simpler to apply than the local interpretable model-agnostic explanations, which require a tuning process, though both offer similar insights. They show that short-period accumulated precipitation is crucial during the steep rising limb of the hydrograph, reaching 72% of importance on average among the top features. As the peak approaches, previous streamflow values become the most influential feature, continuing into the falling limb. When the hydrograph goes down, the model confers a moderate influence on the accumulated precipitation of several hours back of distant regions, suggesting that the runoff from these areas is arriving. Machine learning models may interpret the catchment system reasonably and provide useful insights about hydrological characteristics. Full article

Although Bangladesh is frequently regarded as ‘ground zero’ for climate change, the Chittagong Hill Tracts (CHTs) have only recently been acknowledged for their environmental vulnerabilities, especially after the devastating rainfall and landslides of 2017. However, attributing these risks solely to climate change overlooks their entanglement with structural inequalities, extractive development, deforestation, and long-standing marginalization. The study examines how climate variability intersects with broader environmental risks through a mixed-methods approach, integrating 30 years of NASA TRMM_3B42_daily rainfall data with a household survey (n = 400), life stories, focus group discussions, and key informant interviews conducted across all three CHT districts. Findings do not support a singular attribution to climate change. Rather, they reveal compounded vulnerabilities shaped by land degradation, water scarcity, flash flooding, and landslides—often linked to deforestation and neoliberal development interventions. We argue that the CHT exemplifies ecological entanglement, shaped by climate variability and structural inequalities rooted in land governance and Indigenous dispossession. By integrating spatially disaggregated climate data with historically grounded local experiential narratives, this study contributes to climate justice debates through relational, place-based understandings of vulnerability in the Global South. Full article

A vital step for any hydrochemical assessment is properly carrying out quality assurance and quality control (QA/QC) techniques to evaluate data confidence before performing the assessment. Understanding the processes governing groundwater evolution in coastal aquifers is critical for managing freshwater resources under increasing anthropogenic and climatic pressures. This study reassesses the hydrochemical and isotopic data from the Deep Confined Aquifer System (DCAS) in the Jiangsu Coastal Plain, China, by firstly applying QA/QC protocols. Anomalously high Fe and Mn concentrations in several samples were identified and excluded, yielding a refined dataset that enabled a more accurate interpretation of hydrogeochemical processes. Using hierarchical cluster analysis (HCA), principal component analysis (PCA), and stable and radioactive isotope data (δ²H, δ¹⁸O, ³H, and ¹⁴C), we identify three dominant drivers of groundwater evolution: water–rock interaction, evaporation, and seawater intrusion. In contrast to earlier interpretations, we present clear evidence of active seawater intrusion into the DCAS, supported by salinity patterns, isotopic signatures, and local hydrodynamics. Furthermore, inconsistencies between tritium- and radiocarbon-derived residence times—modern recharge indicated by ³H versus Pleistocene ages from ¹⁴C—highlight the unreliability of previous paleoclimatic reconstructions based on unvalidated datasets. These findings underscore the crucial role of robust QA/QC and integrated tracer analysis in groundwater studies. Full article

Land subsidence significantly threatens urban infrastructure, agricultural productivity, and environmental sustainability. This study develops a land subsidence susceptibility model by integrating Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) data with key geospatial factors using machine learning approaches. The study focuses on the Attica prefecture, Greece, and utilizes SBAS InSAR data from 2015 to 2021 to extract ground deformation velocities by classifying them into four susceptibility levels: stable, low, moderate, and high. The susceptibility results indicate that stable zones constitute 58.2% of the study area, followed by low (27.2%), moderate (11.2%), and high susceptibility zones (3.4%), predominantly concentrated in areas undergoing hydrological stress and urbanization. Random Forest (RF) and XGBoost (XGB) models incorporate a comprehensive set of causal factors, including slope, aspect, land use, groundwater level, geology, and rainfall. The evaluation of the models includes accuracy metrics and confusion matrices. The XGB model achieved the highest performance, recording an accuracy of 94%, with well-balanced predictions across all susceptibility classes. Addressing class imbalance during model training improved the recall of minority classes, though with slight trade-offs in precision. Feature importance analysis identifies proximity to streams, land use, aspect, rainfall, and groundwater extraction as the most influential factors driving subsidence susceptibility. This methodology demonstrates high reliability and robustness in predicting land subsidence susceptibility, providing critical insights for land-use planning and mitigation strategies. These findings establish a scalable framework for regional and global applications, contributing to sustainable land management and risk reduction efforts. Full article