Search Results (591)

The long-term sustainability of Australian agriculture is fundamentally constrained by the capacity, condition, availability, and governance of soil resources. Australian soils are among the oldest and most weathered globally, highly heterogeneous, and often slow or effectively irreversible to recover once degraded. Traditional approaches centred on soil health, while valuable at paddock scale, are insufficient to address national-scale challenges related to spatial variability, data continuity, economic valuation, and policy integration. This paper examines soil security as a policy-relevant framework for supporting more sustainable Australian agriculture. Building on the dimensions of soil security (capacity, condition, capital, connectivity, and codification), we synthesise recent Australian case studies to show how soil security extends beyond soil health to integrate biophysical properties, digital soil infrastructure, socio-economic value, and governance mechanisms. Drawing on recent Australian case studies, this review identifies advances in digital soil mapping, national soil assessments, economic valuation of soil capital, stakeholder connectivity, and emerging policy frameworks, while also identifying persistent gaps in regulation, data standardisation, and institutional coordination. The paper argues that soil security can help operationalise 3-N agriculture—Net-Zero, Nature-Positive, and Nutrient-Balanced systems—by translating sustainability goals into spatially explicit, place-based decisions grounded in soil realities. By explicitly accounting for soil capacity limits, condition trajectories, capital value, information flows, and codified rules, soil security can support more realistic climate mitigation strategies, targeted nature-positive interventions, and durable nutrient security outcomes. We conclude that embedding soil security more explicitly within Australian agricultural research, policy, and governance would strengthen efforts to deliver productive, resilient, and socially legitimate food and fibre systems. Without soil security, sustainability frameworks may remain difficult to operationalise consistently; with soil security, they can be translated more effectively into measurable, place-based, and durable decisions. Full article

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Open-pit mining increasingly substitutes truck-based haulage with continuous systems—such as mobile bridges and relocatable conveyors—to mitigate operational costs and environmental impacts. This PRISMA 2020-compliant systematic review (2010–2025) maps transferable evidence in structural analysis, optimization, and maintenance for truss-type mobile assets. Following a systematic search in Scopus and Web of Science, 94 studies were selected via MMAT quality appraisal and analyzed through cluster-based synthesis. Results reveal sustained publication growth since 2018, with a corpus dominated by finite element (FE) research on steel bridges and capacity assessment, supplemented by emerging areas in AI-driven structural health monitoring (SHM). Given the scarcity of mining-specific literature, bridge engineering serves as a structural proxy for mobile applications. Critical research gaps include full-scale operational validation, soil–structure interaction, and design–maintenance co-optimization. The study concludes with an evidence-anchored agenda toward validated, predictive, and sustainable monitoring frameworks, positioning digital-twin integration as a promising future horizon rather than a current industry-wide convergence. Full article

Accurate mapping of soil total nitrogen (STN) is fundamental for advancing sustainable and precision soil management. While digital soil mapping (DSM) has increasingly relied on Earth observation (EO) data, the potential of various synthetic aperture radar (SAR) features, particularly interferometric coherence and texture, remains underexplored for large-scale STN prediction. This study aimed to systematically evaluate the potential of multiple Sentinel-1 SAR-derived features, including backscatter coefficients, interferometric coherence, and texture metrics, for modeling and mapping STN across the Iberian Peninsula. We integrated 4296 soil samples from the 2018 LUCAS dataset with multi-source environmental covariates processed via the Google Earth Engine (GEE) platform. Nine modeling scenarios were designed to compare individual and combined contributions of Sentinel-1, Sentinel-2, topographic, and climatic variables using random forest (RF) and extreme gradient boosting (XGBoost) algorithms. The results indicated that the selection of SAR-derived features significantly influences prediction accuracy. Among individual Sentinel-1 feature groups, texture metrics and interferometric coherence outperformed the traditionally used backscatter coefficients, emphasizing their effectiveness in STN mapping. Specifically, texture-based and coherence-based models achieved R² values of 0.34 to 0.35 and 0.33, respectively, whereas backscatter-only models yielded the lowest accuracy (R² = 0.29 to 0.30). The integration of all three radar categories substantially improved performance (R² = 0.39 to 0.42), surpassing the performance of models based solely on Sentinel-2 optical data (R² = 0.33 to 0.34). The most comprehensive model, which combined multi-source EO data with topographic and climatic variables, achieved the highest overall accuracy with R² values of 0.51 for RF and 0.52 for XGBoost. Variable importance analysis confirmed that satellite-derived variables were the most influential group. Spatial predictions successfully captured the heterogeneity of STN across the peninsula, with higher concentrations in humid, mountainous regions and lower values in arid central plateaus and southern regions. This study demonstrates that integrating diverse Sentinel-1 radar information, particularly coherence and texture, provides a robust alternative or complement to optical data, offering a powerful tool for large-scale soil property mapping. Full article

Coastal tidal flat soil organic carbon (SOC) is significantly affected by reclamation activities. However, the limited availability of historical SOC data constrains the reconstruction of past SOC. SOC data were integrated in current time-point and remote sensing data during the last two decades by applying machine learning (ML) methods such as random forest (RF), boosted regression trees (BRT), and extreme gradient boosting (XGBoost) to map the spatiotemporal distribution of tidal flat reclamation and the spatial distribution of SOC content in the western coastal region of the Bohai Rim over the last two decades and to explore how the period and type of reclamation affect SOC content. The results show that: (1) The area of tidal flats decreased by 61.92% from 2000 to 2020 due to reclamation activities. (2) Among the ML methods, the XGBoost model demonstrated the best performance (R² = 0.71, MAE = 0.93 g/kg, RMSE = 1.32 g/kg, d-Willmott = 0.98), with the modified normalized difference water index (MNDWI) being the most important predictor variable. (3) The SOC content of tidal flats decreased from 4.11 g/kg in 2000 to 3.33 g/kg in 2020, a reduction of 18.98%. (4) The reclamation of tidal flats into marshes, forest lands, grasslands, farmlands, and bare lands led to an increasing trend in SOC content, with the greatest increase observed in regions converted to farmlands. This study provides data support for the control of reclamation activities, creation of tidal flat conservation policies, and strategic decision-making for climate change mitigation. Full article

Benggang (Benggang), a typical landform characterized by severe erosion and a geohazard in the red-soil hilly regions of southern China, is characterized by a fragmented texture, irregular boundaries, and high similarity to background objects such as bare soil and roads, which poses a dual challenge of “multiscale variability + strong noise” for automated identification at regional scales. To address insufficient information from a single modality and the limited representation of cross-scale features, this study proposes a dual-stream feature-fusion network (DF-Net) for multisource data consisting of a digital orthophoto map (DOM) and a digital elevation model (DEM). The method adopts ResNeSt50d as the backbone of the two branches: on the DOM side, a Canny-edge channel is stacked to enhance high-frequency boundary information; on the DEM side, derived terrain factors, including slope, aspect, curvature, and hillshade, are introduced to provide morphological constraints. In the cross-modal fusion stage, a multiscale sparse attention fusion module is designed, which acquires contextual information via multiwindow average pooling and suppresses noise interference through top-K sparsification. In the decision stage, a multibranch ensemble is employed to improve classification stability. Taking Anxi County, Fujian Province, as the study area, a coregistered dataset of GF-2 (1 m) DOM and ALOS (12.5 m) DEMs is constructed, and a zonal partitioning strategy is adopted to evaluate the model’s generalization ability. The experimental results show that DF-Net achieves 97.44% accuracy, 85.71% recall, and an 82.98% F1 score in the independent test zone, outperforming multiple mainstream CNN/transformer classification models. This study indicates that the strategy of “multimodal feature enhancement + sparse attention fusion” tailored to Benggang erosional landforms can significantly improve recognition performance under complex backgrounds, providing technical support for rapid Benggang surveys and governance-effectiveness assessments. Full article

This Special Issue, entitled “Digital Soil Mapping for Agri-Environmental Management and Sustainability”, gathers nine studies from around the globe that illustrate how digital soil mapping (DSM) is being applied to support agri-environmental management and sustainability. Field- and farm-scale studies are emphasized, where informed decisions are essential for efficient day-to-day management and profitability. The articles highlight the integration of remote/proximal sensing, along with modern machine learning techniques, to produce high-resolution soil maps, soil fertility and nutrient management zoning, and to monitor salinity and soil moisture to inform irrigation and land management. Another key focus is improving sampling strategies and assessing prediction uncertainty and model interpretability. This collection sets future DSM priorities, including cost-effective sampling, robust uncertainty assessments, and reliable cost–benefit and risk assessment approaches that link map accuracy/uncertainty to management outcomes and economic performance. Full article

Predictive Digital Soil Mapping (PDSM) in Eastern Amazonia faces challenges due to its environmental complexity, difficult access, and scarce legacy data. While legacy soil maps contain valuable tacit knowledge, updating them requires methods that can handle uncertainty. This study evaluates the integration of old soil maps with machine learning to update soil information in Tracuateua, Pará, with a specific focus on the performance of ensemble learning and the explicit incorporation of uncertainty metrics in soil mapping units under hydromorphic influence, which, in addition to being difficult to access, are influenced by complex pedogenetic processes. We combined 270 sampling points, equivalent to the total pixels that captured the variability of soil mapping units, with environmental covariates and historical data. Several algorithms were tested, including an ensemble approach, to predict mapping units and quantify uncertainty through entropy and confusion indices. The ensemble model demonstrated improved stability and reduced classification uncertainty compared to single models, particularly in challenging hydromorphic environments. Although accuracy gains were modest, the models captured soil–environment relationships, with climate as: Annual Mean Temperature 22,000 years ago (Tmean_22k), relief: Channel Network Base Level (CNBL and altitude) and organism variables: Land Surface Temperature (LST) emerging as the main predictors. Spatialized uncertainty estimates, expressed through entropy and the confusion index, provide a practical decision-support tool for guiding field surveys and identifying areas of low mapping reliability. By explicitly transferring the pedologist’s mental model—encoded as tacit knowledge in legacy soil maps—into ensemble learning, this study presents a robust and transferable framework for updating soil maps in data-scarce tropical regions, balancing predictive performance, spatial consistency, and uncertainty-aware interpretation. Full article

Land is the fundamental basis for maintaining agricultural production and ensuring food security. The task of managing and sustainably utilizing land resources has always been a priority for every country in the world. The study used GIS-MEC technology to integrate data from seven types of single-factor maps to construct a soil quality map with 47 land units (including eight land units with an area >100 ha, 29 land units with an area from 10 to 100 ha, and 10 land units with an area <10 ha). In addition, by combining soil quality maps and the nutritional needs of different crops, an assessment of land suitability for six major crops was conducted, and three key crops were selected for focused development: rice, vegetables, and flowers. The application of GIS in soil quality management is in line with the current trends of digital transformation and integrated data management in Vietnam and around the world. However, this method has several limitations that need to be considered when applying it, such as dependence on expert expertise, high demands on input data and verification of output results, and limitations in analyzing trends and analyzing social, non-linear factors. Full article

The spatial variability of soil physicochemical properties significantly influences irrigation efficiency, nutrient availability, and the long-term sustainability of irrigated agriculture in semi-arid regions. This study aimed to quantify and model the spatial distribution of soil properties in a semi-arid irrigation district in central Mexico (Irrigation District 001 “Pabellón de Arteaga”, Aguascalientes), providing spatially explicit information for differential irrigation and fertilization management. Ninety-seven crop and four natural sampling sites were established under a stratified random design at two soil depths (0–30 and 30–60 cm). Geostatistical and machine learning models (Ordinary Kriging, OK; Generalized Additive Models, GAM; and Random Forest, RF) were applied to predict spatial patterns, and their performance was evaluated using statistical metrics. The findings reveal high spatial and vertical variability, with most properties (such as organic matter, total nitrogen, and texture) showing significant stratification with depth. In contrast, others (pH and electrical conductivity, EC) remained remarkably homogeneous vertically. Correlation patterns were identified, highlighting the negative influence of alkaline pH (≈8.0) on the availability of micronutrients (Fe²⁺ and Mn²⁺) and the positive association between EC and soluble cations (Ca²⁺, K⁺, and Na⁺). Moran’s Index confirmed significant spatial autocorrelation for most properties, reducing the effective sample size by 30–70%. The comparative evaluation of predictive models demonstrated the superiority of RF over OK and GAMs for predicting chemical properties, thanks to its ability to capture nonlinear relationships and complex interactions. However, the overall predictive performance was moderate, reflecting the multifactorial complexity of the edaphic system. This study lays the foundation for the development of an accessible, low-cost Decision Support System by providing a robust methodological framework for spatial soil characterization and contributing to more sustainable, resilient agriculture, where decision-making is based on quantitative data and predictive models. Full article

Harmonised land evaluation frameworks are essential for sustainable land planning and policy development. Assessing land suitability is crucial for predicting agricultural and forestry potential but also for mitigating land degradation risks. Current land suitability maps in Portugal vary greatly in scale and methodology. This study presents the first nationally consistent framework to produce a harmonised land suitability map for mainland Portugal at a 1:100,000 scale following a recently updated WRB soil map. The latter was obtained by integrating legacy soil data with delineated land units according to soil-forming factors (climate, lithology, and relief). These land units were used to derive key land qualities, subsequently classified into constraint levels. Following FAO land evaluation principles, four land suitability levels for agriculture and forestry were assigned to 125 land units across three representative areas in southern Portugal. Relief and lithology emerged as main drivers of land suitability. Marginal agricultural lands are largely dominant (65.1–78.0%), followed by non-suitable lands (14.8–28.3%). Forestry suitability is mostly confined to moderate (61.5–69.4%) and marginal (30.6–37.4%) classes, reflecting the higher adaptability of forestry systems. High consistency was observed between the derived suitability classes and the latest land use/land cover map of Portugal. The framework enables decision-makers to identify areas suitable for intensive production while safeguarding lands vulnerable to degradation. It also provides a transferable tool for adaptive landscape management and sustainable land allocation, supporting policy development under changing environmental conditions in Mediterranean regions. Full article

Soil pH is a critical soil property governing nutrient availability and ecosystem functioning. Digital mapping of its spatial distribution is essential for precision agriculture and sustainable land management. This study performs a comparative analysis of six tree-based models coupled with residual kriging (RK) for 30 m resolution mapping of soil pH in Shayang County, China. Specifically, random forest (RF), extremely randomized trees (ERT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) were used. Based on 1343 soil samples and 32 environmental variables, experimental results demonstrate that the integration of RK enhanced the prediction accuracy of all standalone models by taking the spatial dependence of residuals into account. Among the models, CatBoost-RK achieved the best performance with an R² of 0.7265, RMSE of 0.5072, and RPD of 1.9122, closely followed by ERT-RK and RF-RK. The analysis of variable importance identified soil type (ST) and mean annual precipitation (MAP) as the most critical factors affecting soil pH distribution. The generated 30 m resolution soil pH map reveals distinct patterns across different land use types, with croplands showing lower soil pH and grasslands exhibiting higher pH with greater variability. These findings confirm the effectiveness of the hybrid ML-RK framework and provide valuable insights for selecting optimal modeling strategies in digital soil mapping. Full article

Burrows are tunnels or holes excavated into the ground by certain types of animals, to be used as habitation or temporary refuge, or as a by-product of their locomotion. Burrows provide a form of shelter against predation and exposure to the elements, and can be found in nearly every biome and among various biological interaction types. River bank burrowing weakens the soil structure, increases the risk of erosion, and may lead to bank retreat and landslides. Currently, burrow watching, mapping, and prevention are human-only activities, and there are no conventional data or information systems designed for this purpose. In this paper, we design, implement, and test a novel AI-based solution that, starting with drone-acquired imagery, allows the user to automatically identify and map potentially dangerous burrows in the target area, and lays the basis for the digitization and systematic conservation of such information, to be later used for intervention and planning. Our solution contributes to the environmental sustainability of rivers, especially close to densely populated areas. Full article

Artificial intelligence (AI) is rapidly affecting soil science by allowing the analysis of large, complex, and heterogeneous datasets that were previously difficult to exploit. The current review synthesizes the recent advances of AI and highlights how these tools are applied in key soil science domains, such as digital soil mapping, soil fertility management, soil moisture prediction, contamination monitoring, soil carbon assessment, and precision agriculture. This study evaluates the performance of different AI methods, showing that techniques such as random forests, neural networks, and convolutional neural networks often outperform traditional methods in capturing non-linear soil-environment. At the same time, it identifies major limitations such as data scarcity, reproducibility, lack of large datasets, uncertainty, and the “black-box” nature of many models. This review concludes that AI has strong potential to support sustainable soil management, but its real-world impact will depend on better data integration, explainability, standardization, and closer collaboration with scientists, technologists, and end-users. Full article

This study aimed to evaluate the effectiveness of spectral absorption-feature indices, derived from soil hyperspectral diffuse reflectance spectroscopy, as covariates within a multivariate geostatistical framework to enhance the digital mapping of soil organic carbon (SOC). The approach also incorporated exhaustively measured auxiliary variables derived from topographic and textural attributes. The research was conducted in a 1.39-km² forested catchment, where 135 topsoil samples (0–0.20 m depth) were collected from soils classified as Typic Xerumbrepts and Ultic Haploxeralfs. All samples were analyzed for SOC concentration, soil texture, and diffuse reflectance spectra across the VIS–NIR–SWIR region (350–2500 nm). The continuum-removal technique was applied to compute radiometric indices associated with absorption features in the visible region and at 1400, 1900, and 2200 nm. Results demonstrated that these indices effectively captured the SOC spatial variability when combined with silt fraction and topographic attributes, which, among the other covariates, actually exhibited the strongest spatial relationships with SOC. Compared to univariate ordinary kriging, the multivariate geostatistical approach yielded improved prediction accuracy in cross-validation, mostly due to the use of hyperspectral indices as auxiliary variables. Moreover, the geostatistical analysis revealed that the multivariate frame of spatial association was characterized by two distinct spatial scales. The findings of this work then support the use of hyperspectral indices as valuable covariates for digital modelling of SOC distribution even in landscapes characterized by heterogeneous topography and pedology. Full article