17 results
Now showing1 - 10 of 17
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Modeling soil solution electrical conductivity across Europe(Elsevier, 2026-07-07); ; ; ; Soil salinization, referring to the excessive accumulation of soluble salts in soils, adversely influences nutrient cycling, biodiversity, soil structure, crop production, soil health, and ecosystem functioning. Accurately assessing soil salinity via electrical conductivity (EC) is key to mitigating its impacts. Thus, developing predictive tools for soil EC at regional and continental scales is essential for sustainable soil management. Here, we apply machine learning models to predict soil EC in the European Union (EU) and United Kingdom (UK) soils using different environmental factors like soil, climate, topography, and satellite data as predictors. The model is trained by ≈40,000 soil EC data points from the 2015 and 2018 Land Use/Cover Area Frame Survey data (LUCAS) surveys, complemented by the EC observations from World Soil Information Services (WoSIS) dataset. To improve the model performance, a forward feature selection technique was used resulting in selection of 17 covariates out of initially 34 predictors. The final selected XGBoost model achieved R<sup>2</sup> values of 0.68, 0.6, and 0.63 for the training, internal testing, and independent validation datasets, respectively. For the year 2018, we estimate ≈21.7 Mha of EU + UK land exceeds an EC of 0.6 dS/m (at a 1:5 soil to water ratio, the so-called EC<inf>1:5</inf>). This estimate should be interpreted as elevated predicted EC<inf>1:5,</inf> rather than a direct estimate of soils meeting protosalic diagnostic criteria. The output of the predictive model consists of a gridded dataset that illustrates the spatial distribution of EC<inf>1:5</inf> throughout the study area for the year 2018, along with an associated uncertainty map with a spatial resolution of 1 km.Publicationtype: Journal Article17 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Soil degradation in Europe is projected to accelerate under changing land use and climate(Springer, 2026-04-04); ; ; ; Soil degradation threatens food security and environmental sustainability, yet future projections of it are rare. Using projections from 18 global climate models under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) and land-use projections from the Land Use and Climate Across Scales Land Use Change (LUCAS LUC) dataset, we assess future soil vulnerability to degradation by linking a Soil Degradation Proxy (SDP) to climate, land-use, soil characteristics, and socio-economic factors at 7433 observation sites across Europe. We project that by 2071–2100, ~59% of sites may become more vulnerable under the high-emission scenario. Cold forest regions in northern Europe are projected to face increased degradation pressure by ~+0.04SDP. However, some European croplands may improve locally through conversion to secondary lands, reduced human pressures, and natural recovery processes. These regionally specific trends highlight that, while soil degradation remains a major threat, proactive land management can mitigate soil vulnerability under future climate trajectories.Publicationtype: Journal Article7 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A global analysis of the influence of shallow and deep groundwater tables on relationships between environmental parameters and heatwaves(Elsevier, 2026-01-15); ; ; ; Heatwaves increasingly impact ecosystems, human health, and economic activities worldwide. As their frequency and intensity rise, understanding the mechanisms driving heatwave dynamics and interactions with land surface processes becomes crucial. While numerous studies have examined atmospheric and land surface variables, the role of groundwater, through its effects on soil moisture and surface evaporative fluxes, remains less understood. Although modeling approaches at various scales have enhanced our understanding of groundwater-atmosphere coupling, machine learning (ML) enables capturing complex, nonlinear interactions and evaluating the relative importance of key drivers globally. We developed pixel-based ML models to estimate global summer heatwave frequency over the past 21 years. For each pixel, we considered data within a 1.5° radius (149 neighboring pixels), identified as the optimal scale through a saturation radius analysis. We used feature importance metrics to identify the dominant drivers among surface fluxes, land characteristics, atmospheric and hydrological variables, and interpreted these results in relation to contrasting groundwater depths (<10 m and >100 m). We ensured robustness using 10-fold cross-validation and confirmed that results were not driven by randomness with two additional validation runs on a subset of the data, with shuffled targets and randomized covariates. Our findings suggest that geopotential height showed the highest relative importance among predictors in regions with deep groundwater tables, while in areas with shallow groundwater, surface fluxes emerge as the key contributor. Incorporating groundwater-related processes may therefore improve understanding of land-atmosphere interactions and support more robust assessments of future heatwave risks.Publicationtype: Journal Article27 32 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Spatial and temporal assessment of soil degradation risk in Europe(Nature Publishing Group, 2025-12-24); ; ; ; Soil degradation threatens agricultural productivity and ecosystem resilience across Europe, yet spatially consistent assessments of its intensity and drivers remain limited. In this study, we used Soil Degradation Proxy (SDP), that integrates four key indicators of soil degradation, including erosion rate, soil pH, electrical conductivity, and organic carbon content, to quantify soil degradation risk. Using over 38,000 LUCAS topsoil observations and a machine learning model trained on climate, land cover, topographic, soil parent material properties, and spectral variables, we map annual SDP values between years 2000 to 2022 across Europe. Results show soil degradation risk is highest in southern Europe, especially in intensively managed and sparsely vegetated landscapes. Over the past two decades, approximately 7.1% of land area across the EU and the UK has experienced increasing degradation risk (most notably across Eastern Europe), with rainfed croplands emerging as the most affected land cover type. Land cover is the most influential driver, modulating effects of climatic variables such as precipitation and temperature on SDP. This data-driven framework provides a consistent and scalable approach for monitoring soil degradation risk and offers actionable insights to support targeted conservation and EU-wide policy implementation.Publicationtype: Journal Article21 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Rethinking global soil degradation: drivers, impacts, and solutions(American Geophysical Union (AGU), 2025-12); ; ; ; The increasing threat of soil degradation presents significant challenges to soil health, especially within agroecosystems that are vital for food security, climate regulation, and economic stability. This growing concern arises from intricate interactions between land use practices and climatic conditions, which, if not addressed, could jeopardize sustainable development and environmental resilience. This review offers a comprehensive examination of soil degradation, including its definitions, global prevalence, underlying mechanisms, and methods of measurement. It underscores the connections between soil degradation and land use, with a focus on socio‐economic consequences. Current assessment methods frequently depend on insufficient data, concentrate on singular factors, and utilize arbitrary thresholds, potentially resulting in misclassification and misguided decisions. We analyze these shortcomings and investigate emerging methodologies that provide scalable and objective evaluations, offering a more accurate representation of soil vulnerability. Additionally, the review assesses both physical and biological indicators, as well as the potential of technologies such as remote sensing, artificial intelligence, and big data analytics for enhanced monitoring and forecasting. Key factors driving soil degradation, including unsustainable agricultural practices, deforestation, industrial activities, and extreme climate events, are thoroughly examined. The review emphasizes the importance of healthy soils in achieving the United Nations Sustainable Development Goals, particularly concerning food and water security, ecosystem health, poverty alleviation, and climate action. It suggests future research directions that prioritize standardized metrics, interdisciplinary collaboration, and predictive modeling to facilitate more integrated and effective management of soil degradation in the context of global environmental changes.Publicationtype: Review Article22 31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Patterns and thresholds for soil pH across Europe in relation to soil health and degradation(Elsevier, 2025-12-01); ; ; ; Soil pH indicates the level of acidity or alkalinity in the soil environment, influencing various biogeochemical and physical processes. Additionally, soil pH levels are crucial in determining the bioavailability of elements such as iron, aluminium, and heavy metals which can be harmful. As such, pH is an important soil health and degradation indicator. Although there is a well-established understanding of soil pH at localized levels, the spatial and temporal variations, as well as significant thresholds at national and continental scales, are not sufficiently documented. Here we analyse the European topsoil pH data (LUCAS) in combination with other soil properties from the LUCAS survey, to identify thresholds and spatial patterns of soil pH across Europe in relation to soil health and degradation. At the European scale we found: 1) the water balance, calculated as mean annual precipitation minus potential evapotranspiration (MAP-PET), provides essential context to interpret soil pH; 2) the shift from organic carbon-rich soils to those dominated by inorganic carbon is observed at a pH of about 7.2, however, soil moisture levels may be more critical than pH for the accumulation of soil organic carbon; 3) we identified three distinct clusters within the multivariate regression tree: acidophiles (below pH 5.2), neutrophiles (pH 5.2–6.9) and alkaliphiles (above pH 6.9), while optimum microbial diversity occurred between pH 6 and 7. Earthworm abundance, as reported by the sWorm database, is more nuanced and dependent on land use; 4) risk of degradation by heavy metals cannot be captured by a single pH threshold. Finally, we identify soil pH thresholds that can aid policymakers in identifying regions that may require protection or intervention.Publicationtype: Journal Article6 28 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A multi-scale framework for assessing the suitability of low impact development in a dense flood-prone urban areaLow Impact Development (LID) strategies offer multiple benefits, including flood prevention, urban diffuse pollution control, and the creation of alternative water sources by increasing permeable surfaces. This study applies a multi-scale, multidisciplinary holistic and adaptive strategy framework for flood management in a high-density urban area in Ankara to identify suitable zones for LID implementation. The framework integrates field research, remote sensing, statistical learning, and expert input bridging engineering and urban planning perspectives, consist of (i) LID land suitability at the pixel scale and (ii) LID planning with prioritization at the neighborhood scale. Land Use and Land Cover classification was conducted on Google Earth Engine using the Random Forest algorithm and WorldView-4 satellite imagery. Six commonly used Multi-Criteria Decision-Making (MCDM) methods were employed to assess land suitability using MetaRanking. Neighborhood-scale analysis identifies priority zones for LID implementation by evaluating building density, runoff accumulation, and the intersections of impermeable surfaces with high-density areas. According to MCDM findings, bioretention cells are the most suitable LID type (29.8 %), while rain barrels account for 24.1 % of building coverage. The suitability analysis highlights the northwestern region of the study area as a priority zone for LID applications. Proposed green corridors and permeable pedestrian pathways can further enhance water retention and urban resilience. The plant species selected for LID systems are drought-tolerant, region-specific, and derived from floristic studies conducted in Ankara, considering both ecological and aesthetic contributions. This holistic approach addresses the complexities of urban hydrology, spatial planning, and socioeconomic dynamics, promoting sustainable and equitable urban landscapes.Publicationtype: Journal Article12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Continental scale soil monitoring: a proposed multi-ccale framing of soil qualityGlobally, soils are subjected to various management practices and stressors which can lead to degradation. This makes their protection essential for sustaining many functions and services as well as maintaining the overall life support system of Earth. National monitoring programmes are increasingly implemented to evaluate the state and trend of soils, a move which has been advocated by the Mission Soil in Europe. In soil science, frameworks have been established to interpret and communicate soil monitoring results, concentrating on the concept of quality, a term which can be interpreted in many ways. This paper explores the multifaceted meaning of soil quality, addressing its implications for future soil health assessments. It achieves this by focusing on the context of the Mission Soil. Soil health is a holistic concept embracing emergence, complexity and highlighting long-term vitality and resilience. In contrast, soil quality is often viewed through the lens of its capacity to meet specific human needs and functions, typically in a shorter timeframe. The concept of quality is assessed through indicators where the choice of framework significantly influences selection and interpretation. However, selecting appropriate soil indicators across Europe is challenging due to diverse climate, topography, geology and soil types, resulting in varied soil processes. Therefore, establishing clear principles and criteria for soil indicator selection is essential. Our paper identifies four distinct frameworks for soil quality assessment: ‘Fitness for Purpose’, ‘Free from Degradation’, ‘External Benchmarking’ and ‘Value Assessment’, with each possessing a unique role and application. Notably, the ‘Free from Degradation’ framework is emphasised for its alignment with soil protection efforts and its relevance to soil threats. This makes it particularly suitable for pan-European assessments conducted by the European Union Soil Observatory (EUSO).Publicationtype: Review Article18 26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Impacts of land use change on nutrient balance and greenhouse gas emissions: a regional perspectiveNutrient balance is critical for sustainable land management, yet information scarcity hampers its systematic evaluation of trade-offs among alternate land uses. We employed a detailed regional nutrient dataset collected from 70 monitoring sites over 16 years to conduct a comprehensive analysis of yields, nutrient balances and greenhouse gas emissions associated with different land management practices in Lower Saxony, Germany. The information was used to develop land use transformation scenarios while assessing their impacts on regional nutrient balances and emissions. Our analysis demonstrated that organic farming exhibited lower nutrient surpluses but also lower yields compared to conventional systems, while grazing systems showed the highest nutrient outputs. A comparison with other regional studies highlights the importance of unique combinations of climate, soil, management practices, and socioeconomic settings in developing sustainable land management strategies – a global perspective, while useful in setting goals, may not capture local needs specific to this combination of factors.Publicationtype: Journal Article9 31 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Spatial and temporal assessment of soil salinization across EuropeSoil salinization, referring to the excessive accumulation of soluble salt in soils, adverselyinfluences nutrient cycling, microbial activity, biodiversity, plant growth, and crop production, thus affecting soil health and ecosystem functioning (Shokri et al., 2024). Soil salinity quantification is a major step toward the mitigation of its effects. Therefore, developing quantitative tools to predict soil salinity at regional and continental levels under different boundary conditions and scenarios is crucial for sustainable soil management and the security of natural resources (Hassani et al., 2020, 2021). This study proposes an AI-driven soil salinity quantification and projection approach focused on EU soils using a set of environmental covariates, which consist of soil properties, terrain attributes, climate, and remotely sensed variables. A key aspect of this study is the integration of soil salinity point data from the LUCAS survey in the AI model, complemented by the WoSIS dataset. To improve the model performance, forward feature selection technique was applied. The model achieved the training, testing, and validation accuracy, expressed in , of 0.7, 0.7, and 0.57 respectively. The analysis indicates that 4.9 and 0.6 Mha of the EU land exceeds the 1 and 2 dS/m of electrical conductivity, respectively, highlighting the regions of concern. Italy, Spain, and France show high levels of soil salinity respectively. The output of the predictive model will be a gridded dataset illustrating the spatial and temporal (yearly) distribution of soil salinity across the EU, accompanied by the corresponding uncertainty map with a spatial resolution of 1 km. This information is crucial for identifying regions with elevated salinity levels and formulating necessary action plans to mitigate the situation.Publicationtype: Conference Paper20 12