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The directive on soil monitoring and resilience (Soil Monitoring Law) was included in the Official Journal of the European Union (EU 2025/2360) on November 26th, 20225. After water and air, this Directive puts forward the first EU legislation on the soil environmental compartment. The directive establishes a harmonised soil monitoring framework for assessing the health of soils throughout the EU. A harmonised approach implies that standards become important to ensure intercomparability among, and within, Member States. The directive prescribes reference methodologies and requires transfer functions for other methods. The Directive also includes some standards for the monitoring and measuring of soil descriptors based on those being used through the LUCAS topsoil surveys from 2009 to 2022. The present technical report provides an inventory of 574 soil-related standards from international and European standard bodies including the International Organization for Standardisation (ISO) and the European Committee for Standardisation (CEN), as well as standards from other well-recognised international institutions including the United States Environmental Protection Agency (EPA) and the Food and Agriculture Organization of the United Nations (FAO-GLOSOLAN). There is a need for harmonisation of soil-related standards to ensure consistency and comparability of soil data and to support effective soil management and protection effort.

Download the report: https://esdac.jrc.ec.europa.eu/public_path//shared_folder/doc_pub/JRC144540_01.pdf

Managing soils to increase organic carbon storage presents a potential opportunity to mitigate and adapt to global change challenges, while providing numerous co-benefits and ecosystem services. However, soils differ widely in their potential for carbon sequestration, and knowledge of biophysical limits to carbon accumulation may aid in informing priority regions. Consequently, there is great interest in assessing whether soils exhibit a maximum capacity for storing organic carbon, particularly within organo–mineral associations given the finite nature of reactive minerals in a soil. While the concept of soil carbon saturation has existed for over 25 years, recent studies have argued for and against its importance. Here, we summarize the conceptual understanding of soil carbon saturation at both micro- and macro-scales, define key terminology, and address common concerns and misconceptions.

Loss of soil organic carbon (SOC) from farmland is a key threat to the capacity of soils to provide ecosystem services and exacerbates climate change. In alignment with a published protocol, we conducted a review and meta-analysis of time series of SOC measurements in long-term agricultural experiments to study absolute SOC changes under different agricultural management regimes.

Shifting towards healthy, plant-based diets is widely recognized as a strategy to reduce greenhouse gas emissions (GHG) from food systems, primarily through reduced methane emissions from livestock. However, the implications of this transition for soil-based GHG emissions, a major contributor to climate change, remain uncertain. We used the MAGNET economic model and the DayCent biogeochemical model to assess the impacts of dietary shifts aligned with the EAT-Lancet guidelines on soil organic carbon (SOC), nitrous oxide (N2O) emissions, and the soil GHG balance across the European Union and the United Kingdom. Adopting the EAT-Lancet diet reduced livestock production, organic carbon (C) and organic nitrogen (N) inputs from manure, and permanent grassland areas for agricultural use. This results in potential SOC losses of an EU average of 14 Mg CO2e ha−1 and reaching up to 50 Mg CO2e ha−1 in livestock-intensive regions by 2100. However, afforestation of land released from production could offset approximately half of the diet-induced soil C losses by 2100. When above-ground biomass from afforestation is factored in, this could yield an additional 65 Mg C ha−1 in afforested areas, resulting in net CO2 removal at the European scale. N2O emissions exhibited more moderate and heterogeneous changes by 2100, ranging from 10 to −13 Mg CO2e ha−1 across the continent, and dependent on land use change (LUC) and increased synthetic N inputs. The changes in SOC were driven by LUC, lower organic inputs, soil types and, to a lesser degree, climatic zones. This study's findings underscore the importance of dietary changes in tackling climate change. However, practitioners and policymakers should carefully consider potential soil-related trade-offs by supporting and implementing appropriate soil conservation practices, such as no-tilling or afforestation, to realize the full co-benefits of more sustainable diets.

The Amazon rainforest is crucial for the global carbon cycle, yet annual changes in its aboveground biomass carbon (AGC) stock remain highly uncertain. Natural and local anthropogenic drivers such as deforestation, forest degradation, and regrowth following deforestation interact with large-scale climate variability to determine AGC dynamics. Here, we propose an approach to disaggregate low-frequency passive L-band microwave data over 2010-2020 and reconstruct maps of annual change. We show that the Amazon lost −0.37 ± 0.17 PgC, with gains by undisturbed (0.33 ± 0.13 PgC) and secondary forest growth (0.33 ± 0.05 PgC) outweighed by losses by deforestation (−0.55 ± 0.04 PgC), degradation (−0.42 ± 0.08 PgC), and agricultural areas (−0.06 ± 0.03 PgC). Losses in human-influenced land intensified over time and amounted to 60% of all gross losses in El Niño years. Our study reinforces the need for stronger implementation of policies and effective actions to control forest degradation.

The European Union's strategic agenda for 2024–2029 prioritizes a prosperous and competitive Europe, with soil health potentially playing a role in achieving this goal. However, the current state of European soils is of concern, with over 60 % of soils not in healthy condition, as reported by the European Union’s Soil Mission Board and the EU Soil Observatory. This results not only in environmental issues, but also economic ones, as the costs of soil degradation in the EU are estimated to be higher than €50 billion per year, underscoring the need for soil health to be placed more prominently on the political agenda. Soil-related business models, including biotechnology, remediation of contaminated sites, carbon removals and farming, regenerative agriculture, and agritech solutions, can contribute to EU competitiveness. These business models may help address most of the challenges posed by soil degradation, climate change, and biodiversity loss, while promoting sustainable agriculture practices and improving ecosystem functioning. The EU's soil remediation market is valued at €8.5 billion, with an annual growth rate of 5 %. The EU Carbon Removals and Carbon Farming Regulation provides a framework for certifying carbon removals, with potential revenue of €6 billion per year. Regenerative agriculture, which prioritises soil health and ecosystem services, can increase crop yields, reduce dependency on synthetic fertilisers and pesticides, and promote biodiversity. Agritech solutions, such as precision agriculture and artificial intelligence, can optimize farming practices, reduce costs, and improve environmental sustainability. Here we present the potential of soil-related business models to contribute to EU competitiveness, while addressing environmental and societal challenges. However, a number of challenges remain and need to be addressed as the need for acceleration, a clear policy framework, a closer collaboration of different actors in the food supply chain and a digital transformation are still needed.

Over 60% of European soils are unhealthy according to the Soil Mission board estimates and the indicators presented in the European Union (EU) Soil degradation dashboard. The situation may worsen if no policy interventions are taken. The unsustainable use of natural resources, in particular the degradation of soils, precipitates biodiversity loss, exacerbated by the climate crisis. In particular, in the EU alone, soil degradation costs over €50 billion per year due to the loss of essential services they provide and to the impact on human health. Here a more precise estimation of the soil degradation cost related to a set of soil degradation processes, ranging between €40.9 and 72.7 billion per year is presented. This newly updated estimate compared to the Impact assessment of the Soil Monitoring Law takes into account the costs of soil erosion, contamination, phosphorus losses, soil carbon losses, nitrogen losses, soil compaction, and soil sealing. However, this estimation might double if it is added to the costs of soil biodiversity loss, floods, droughts, off-site effects of soil erosion, and health consequences of soil contamination. Therefore, further research is needed to address this knowledge gap and estimate the missing costs. Soil degradation is a critical issue with transboundary implications that requires urgent attention and action at the EU level. The costs of soil degradation are substantial, both in terms of environmental impacts and economic consequences, highlighting the importance of investing in sustainable soil management practices and a harmonized EU soil monitoring system. By addressing soil degradation through the proposed Soil Monitoring Law, investing significant amounts for research and innovation in the Soil Mission, and promoting international cooperation, the EU can take solid steps toward protecting its soil resources and achieving a sustainable future for all.

Soil Electrical conductivity (EC) is a measure of the ability of soil to conduct an electric current, which is primarily influenced by the concentration of soluble salts in the soil solution that takes place principally through water-filled pores. Ions (Ca2+, Mg 2+, K +, Na +, and NH 4+, SO42-, Cl-, NO3–, and HCO3–) from soluble salts dissolved in soil water carry electrical charges and conduct the electrical current. EC is considered a proxy of soil salinity and other soil characteristics, whose monitoring is much needed in the context of climate change, increasing irrigation in agricultural areas and sea level rise. The pan-European LUCAS soil monitoring scheme, established in 2009, provided EC1:5 in the topsoil (0–20 cm) in the surveys of the years 2015 and 2018 for almost 20,000 samples. In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. The final model resulted in an R2 of 0.302 with an RMSE of 0.265 dS m−1 for soil samples not used for model calibration. The results are presented as predicted ECe in the topsoil, and they reveal that in Atlantic and Northern Europe, salts may accumulate in soils through several natural processes, i.e., primary salinization, but in Mediterranean and Southern Europe, they accumulate because of human interventions on the soil water and solute regimes. 

Soil pollution poses a significant threat to human health and the environment in the Western Balkans. It contaminates food and water sources with potentially toxic elements and degrades ecosystems by reducing soil functions and biodiversity. Industrialization over the past century has made soil pollution a widespread issue in the region. This study aims to summarize the status of point source soil pollution, identify knowledge gaps, and support the implementation of the Green Agenda for the Western Balkans, with a focus on soil remediation priorities.

Conservation benefits from dietary change are commonly assessed without accounting for different conservation objectives. By representing fine-scale habitat and landscape change within a dynamic land-system model, we assess how a partial or full transition to healthier diets would affect indicators across the ‘Nature for Nature’ and ‘Nature for Society’ conservation value perspectives. We find that most diet-related conservation benefits are already achieved by a partial shift to healthier diets. This is because, particularly in many countries in tropical Africa and Asia, adopting healthier diets would mainly involve substituting staple foods with more varied plant-based foods rather than replacing resource-intensive livestock products. Conservation action in line with the Global Biodiversity Framework, by contrast, most consistently improves outcomes across both value perspectives, even under current demand trends, showing that spatial planning is central for decoupling conservation outcomes from food demand. However, any progress towards healthier diets not only lowers greenhouse gas emissions but also reduces barriers to effective conservation, such as higher food prices and imports.

Evaluating heavy metals bioavailable is crucial for comprehensive soil contamination assessment but challenging at large scales due to complex and resource-intensive analytical procedures, and the amount of dissolved metal in soils represents the relative solubility and potential mobility of cadmium, which is a key factor determining bioavailability. Here, we developed a geochemical-integrated machine learning framework using multi-source data to predict cadmium speciation distribution in European and Chinese non-industrial topsoils.

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.

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.

Soil health is critical for sustaining ecosystem functions and addressing environmental challenges. Effective soil health management requires reliable methods for assessing soil properties. Soil spectroscopy may allow resource-effective assessment of soil properties, but more knowledge is needed to transfer knowledge from laboratory-grade spectrometers to in-field data acquisition. This study explores the predictive potential of selected spectral subsets from the full visible and near-infrared (VIS–NIR) range, using various machine learning algorithms (MLAs), as a theoretical exercise to support the design of practical soil sensing tools. Specifically, we evaluated whether narrower spectral ranges can provide predictions comparable to those achieved with the full VIS–NIR spectrum. The ranges are chosen to emulate the spectral coverage and resolution of commercially available sensors which are candidates for widespread and resource-effective data collection. We used the VIS–NIR spectral data (400–2500 nm) alongside laboratory analyses of several soil properties to be predicted from the pan-European LUCAS dataset. We employed four different MLAs for estimating soil properties: support vector regression (SVR), cubist, random forest (RF), and multi-layer perceptron (MLP), which were benchmarked against ordinary least squares regression. Our results showed that spectral subset ranges of 1000–2500 nm and 1350–2500 nm (emulating Trinamix and NeoSpectra sensors, respectively) yielded prediction accuracies similar to the full spectrum. Spectral subsets limited to the visible and early NIR range (350–1000 nm) were less effective. The most informative spectral features were found in wavelengths above approximately 1750 nm. Among MLAs, MLP consistently delivered the best performance, particularly when estimating organic carbon, nitrogen, pH and clay, which were predicted with greater accuracy compared to potassium (K), phosphorus (P) and coarse fragments (CF) which cannot yet be robustly predicted from spectral data alone. This study provides preliminary insight into the spectral regions most relevant for soil property prediction. These findings may inform future development and optimisation of real-world soil sensors. Validation with actual sensor data, both on dried and in situ samples, remains an important next step.

Heavy rainfall is the main driver of water-induced soil erosion, necessitating accurate spatial and temporal predictions of rainfall erosivity to predict the soil erosion response. This study evaluates the ground radar-based EUropean RADar CLIMatology (EURADCLIM) precipitation grids to quantify rainfall erosivity across European countries. Compared to Global Rainfall Erosivity Database (GloREDa) gauge-based interpolations, EURADCLIM overpredicts rainfall erosivity, principally due to residual artefacts in some regions which inflate the instantaneous rainfall rates. Overprediction is most pronounced in European regions with lower radar antenna coverage and complex topography, whereas flatter regions with lower erosivity and better radar coverage are better predicted spatially but with a tendency towards underprediction. Disagreement attributes to the input radar quality in EURADCLIM (derived from OPERA) and to a lesser extent the uncertainty in GloREDa due to its limited gauge records in some regions. Event (EI30) time series analysis showed reasonably good performance (Kling-Gupta Efficiency (KGE) > 0.4) in 50 % of the evaluated gauge locations, although significant overprediction by EURADCLIM was evident in the upper quantiles in some countries. To account for the propagation of these remaining single-hour rainfall artefacts, which have a large impact on the temporally-aggregated R-factor, applying a 80 mm h−1 threshold to limit the maximum I30 value (i.e., less than 0.1 % of GloREDa events exceed this threshold) during the calculation of rainfall erosivity significantly improves the performance of the EURADCLIM dataset at annual, monthly and event time scale. Following adjustment, EURADCLIM best agrees with GloREDa across Europe in July and August, while bigger differences were observed in June and winter in general. Annually, the spatially aggregated rainfall erosivity per country had a percent bias below 10 %. While applying simple I30 thresholds is promising, radar artefacts remain significant in areas with lower quality rainfall retrievals. In the absence of spatiotemporally continuous, high-quality ground-radar retrievals across Europe, we show the value of ensemble R-factor layers of EURADCLIM with three other rainfall erosivity grids (e.g., satellite retrievals) and discuss the possibility of ground radar to offer unique spatial detail in such ensembles.

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.

Ecosystem functioning is potentially dependent on the relationships between soil microbial diversity and biomass. Yet, it remains unclear how land use and climate influence these relationships. Here, we (i) analysed relationships and ratios between richness and biomass of bacteria and fungi in ~500 soils across Europe, including three land-use types (woodlands, grasslands and croplands) and climates (cold, temperate and arid) and (ii) identified the driving factors of changes in richness:biomass (R:B) ratios.

Erosion models simulating the intra-annual effects of hydrometeorological drivers and disturbances (e.g. vegetation clearcutting, tillage events, wildfires) need to represent temporal variability at time scales below the long-term annual average (e.g. the native timescale of the (Revised) Universal Soil Loss Equation). Here, we test a low-complexity, spatially distributed model (WaTEM/SEDEM: W/S), to simulate 15-day erosion and sediment dynamics. A standardised modelling routine was applied to four monitored and well-studied catchments in North-West Europe with open-access discharge (Q) and suspended sediment load (SSL) data, creating a model workflow implementable with predominantly pan-European Union data.

Effective implementation of Sustainable Land Management (SLM) remains a major challenge worldwide because of its weak integration within the domains of science, policy, and development practice. Based on global analyses of soil erosion risk and the degree of implementation of SLM research, policies, and practices at the country level, we propose a transdisciplinary framework to address soil erosion through SLM. In the analysis, we used indices of the policy–development, science–policy, and science–development interfaces to evaluate the overall science–policy–development interface (SPDI) in 236 countries.

Rainfall erosivity maps for (near) real-time soil erosion predictions require the integration of (combinations of) reanalysis products and satellite-retrievals of rainfall, and the overcoming of potential bias related to their simplified spatial and temporal scale. Across Europe, we evaluate: 1) the European Meteorological Observations (EMO) dataset to simulate the localised characteristics of rainfall erosivity at the event scale (EI30), and 2) different implementations of quantile delta mapping (QDM) bias correction to improve the prediction skill. Between 1990 and 2014, evaluations were made at several spatial (location-specific, climatic zone and pan-European) and temporal (event, annual and long-term annual average) scales. The uncorrected EMO predictions demonstrated: 1) a slight overprediction of the number of EI30 events, 2) a reduced coefficient of variation in the EI30 (CV EMO = 1.57, CV REDES = 2.5), and 3) a relatively low (R2 = 0.22, n = 139,306) location-specific predictive skill, with higher discrepancies in all cases in Southern Europe. Following QDM, the EI30 predictions significantly better represented the large-sample variability of EI30 per climate region and improved the monthly correspondence. 

Physical and chemical erosion associated with water both affect land–atmosphere carbon exchanges. However, previous studies have often addressed these processes separately or used oversimplified mechanisms, leading to ongoing debates and uncertainties about erosion-induced carbon fluxes. We provide an overview of the on-site carbon uptake fluxes induced by physical erosion (0.05–0.29 Pg C yr−1, globally) and chemical erosion (0.26–0.48 Pg C yr−1). Then, we discuss off-site carbon dynamics (during transport, deposition, and burial). Soil organic carbon mineralization during transport is nearly 0.37–1.20 Pg C yr−1 on the globe. We also summarize the overall carbon fluxes into estuaries (0.71–1.06 Pg C yr−1) and identify the sources of different types of carbon within them, most of which are associated with land erosion.

Gully formation is a significant driver of soil erosion and land degradation worldwide and often leads to important downstream impacts. Nonetheless, our understanding of the global patterns and the factors controlling this process remains limited. Here, we present the first global assessment of gully density's spatial patterns. Using mapped observations from over 17,000 representative study sites worldwide, we trained random forest models that simulate both the susceptibility to gullying at a 1 km2 resolution and the corresponding gully head density (GHD).map

The global increase in pesticide use has raised concerns about its impact on biodiversity, ecosystems, and human health, in particular of people living near agricultural areas. This study explores the assessment of pesticide exposure and risks to residents at a high spatial granularity using plant protection product data. Our objective was to develop an indicator to monitor pesticide risk levels faced by residents in France by integrating spatial datasets and exposure assessment methodologies. Using spatialized pesticide sales data based on crop authorizations, we mapped potential pesticide loads at the parcel level.

Comparability of soil data derived from different sources is crucial to obtain consistent results when evaluating the soil health status. Discrepancies may arise due to various factors, including uncertainties resulting from different sampling methods. In this study, we compared various soil Physicochemical properties (ST)—pH, organic carbon, texture, cation exchange capacity, nutrients, heavy metals—and microbial diversity (BIO) of samples collected following both the LUCAS Soil (performed by the European Commission Joint Research Centre, JRC) and the Italian (performed by two regional agencies) procedures. The aim was to evaluate the effect of applying different soil sampling protocols on ST and BIO data. Soil samples from 58 LUCAS Soil 2022 sampling sites located in northern Italy were collected following both sampling protocols.