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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.