Search Results (1,315)

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Intensive agricultural practices based on continuous monocropping and prolonged bare-soil fallows have contributed to soil degradation and loss of biological functioning. Replacing fallows with cover crops (CCs) is a promising strategy to restore soil quality, yet their legacy effects on rhizosphere fungal communities remain poorly understood. This study evaluated the legacy effects of Urochloa (syn. Brachiaria) brizantha cover cropping on rhizosphere fungal communities, as well as soil physicochemical and biological properties, in a degraded common bean system. A field experiment with a randomized complete block design included: bare fallow (BM), one (B1) or two (B2) CC cycles before bean, a perennial pasture (PB), and a pristine soil reference (PS). High-throughput sequencing showed that Urochloa-based treatments significantly shifted fungal community composition compared to BM, increasing saprotrophic and beneficial taxa (e.g., Mortierella, Penicillium, Coprinellus) and reducing potential pathogens such as Fusarium. These changes were associated with higher soil organic carbon, aggregate stability, microbial biomass, and enzyme activities, especially in B2 and PB. Indicator taxa identified by LEfSe were linked to organic matter decomposition and nutrient cycling. Multivariate analyses revealed strong associations between fungal community structure and soil properties. Overall, U. brizantha cover cropping induced measurable legacy effects, promoting soil biological recovery even after short-term implementation. Full article

Soil particle size fractions (PSFs)—sand, silt, and clay—are fundamental determinants of soil hydrological behavior, nutrient retention, and erodibility, yet their spatial prediction remains challenging due to the compositional nature of the data, unquantified prediction uncertainty, and limited interpretability of machine learning models. This study develops an integrated compositional mapping framework incorporating multi-source Sentinel-1/2 and topographic covariates, coupling the isometric log-ratio (ILR) transformation with Quantile Regression Forests (QRFs), a Monte Carlo simulation (MCS)-based latent-to-physical space uncertainty propagation strategy, and a Wrapper-SHAP attribution method to jointly address these challenges. The framework was evaluated across regional croplands in the central Shandong mountain-hilly region of China, using an elevation-stratified spatial cross-validation. Validations achieved R² values of 0.72, 0.61, and 0.59 for sand, silt, and clay, respectively, and a global Aitchison distance of 0.34. Critically, the MCS error propagation strategy effectively compensated for the probability distribution shift introduced by non-linear ILR back-transformation. This ensured that all predicted compositions strictly satisfied compositional closure and the [0, 100%] constraint, while aligning the prediction interval coverage probability (PICP) of each fraction closely with the 90% nominal level. Wrapper-SHAP overcame direct attribution limitations in compositional models, revealing the predictive associations of these multi-source covariates: high remote sensing-derived Bare Soil Index (BSI) and Moisture Stress Index (MSI) values primarily exhibited strong predictive associations with sand enrichment, whereas their lower values, combined with elevated Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and anthropogenic indicators, favored silt and clay accumulation. The proposed framework provides a transferable methodological reference for remote sensing-integrated compositional soil mapping with reliable uncertainty estimates and interpretable driver identification at regional scales. Full article

Coastal Louisiana continues to experience rapid wetland loss, increasing the exposure of marsh-creation containment dikes to storm-driven waves, erosion, and sediment loss. This study evaluated offshore-to-nearshore wave transformation, erosion risk reduction, wave runup, and hydrodynamic loading at a representative marsh-creation site in Plaquemines Parish, Louisiana. A 25-year return-period offshore wave condition was derived from long-term Wave Information Study hindcast data and propagated using the SWAN spectral wave model. Two idealized foreshore conditions were examined: a bare-bed case and a marsh-roughened shallow water case represented through enhanced bottom friction. Web Soil Survey data were used to characterize the local soil context of the containment-dike zone. The results show strong wave attenuation across the inner shelf and marsh platform. Relative to the bare-bed case, marsh roughness reduced dike toe significant wave height by 16.1–27.4% and decreased the Hs²-based erosion exposure proxy by 29.6–47.4% across three still-water levels. These reductions produced 15.4–26.4% lower 2% exceedance runup and 28.5–45.8% lower quasi-hydrostatic loading on the containment dike. The results indicate that marsh-induced dissipation can help reduce erosion potential and support sustainable coastal restoration infrastructure management. Full article

Soil water retention capacity (SWRC) is vital for agriculture and watersheds, but traditional measurements are hindered by destructive sampling and spatial discontinuity. This study selected Youyi and Heshan farm in Heilongjiang Province as the study area, using the time-series Normalized Difference Water Index (NDWI) from Sentinel-2 during the snowmelt-to-bare-soil window as a soil water retention signature (SWRS) for monitoring SWRC. The exponential decay fitting model (EDFM) was used to construct a Soil Moisture Decay Index (SMDI) to analyze the spatial patterns of the SWRC. Results showed that: (1) time-series NDWI exhibited distinct exponential decay signatures varying with soil textures and degradation gradients; (2) the EDFM effectively fitted the time-series NDWI (R² = 0.84–0.99), extracting decay rate and stable level to quantify SWRC; (3) SMDI showed high consistency with in situ soil moisture (R = 0.82–0.88) and measured field capacity (Youyi Farm: R² = 0.56; Heshan Farm: R² = 0.59), and correlated significantly with soil organic matter (R² = 0.61–0.71) and texture (R² =0.50–0.64), confirming the physical controls on water retention; and (4) SMDI spatial distribution revealed distinct degradation patterns across varying topographic and soil conditions. This study innovatively transformed point-scale static SWRC measurements into spatially continuous monitoring, offering new tools for precision water management and degraded-soil restoration, with strong theoretical and practical value. Full article

Biological soil crusts (BSCs) play key roles in arid, semi-arid regions and ecological marginal habitats. This study focused on four types of sand-fixing plantations established in 1990 in alpine sandy land (Salix psammophila, SL; Caragana korshinskii, NT; Salix cheilophila, WL; Populus simonii, XYY). Soil samples were collected from bare sand, algae crusts, and moss crusts. Soil particle size distribution, physicochemical properties, and enzyme activity were determined. Then bacterial communities were analyzed using high-throughput (Illumina) sequencing and the correlations among these three factors were examined. The results showed that: (1) From bare sand to algae and moss crusts, the content of fine particles (clay + silt) gradually increased. (2) Soil water content (SWC), nutrients and enzyme activities increased progressively. (3) In the study area, the dominant bacterial phyla of BSCs included Pseudomonadota, Cyanobacteria, Actinobacteriota and Vibrionota. Principal Coordinates Analysis (PCoA) and Analysis of Similarities (ANOSIM) results showed that BSCs drive the differentiation of bacterial communities during succession, while forest stands influence their spatial distribution. (4) Spearman’s correlation and redundancy analysis (RDA) showed that available phosphorus (AP), alkaline hydrolyzable nitrogen (AN), soil organic matter (SOM), catalase (CAT), pH, soil water content (SWC), and alkaline phosphatase (ALP) are key physicochemical factors shaping the bacterial community structure of BSCs. Mantel’s test confirmed that these variables mediated BSCs’ bacterial community structure. This study elucidates the mechanisms underlying ecological restoration via BSCs and provides a theoretical basis for future restoration efforts in alpine sandy land. Full article

Karst critical zones in Southwest China are highly vulnerable to soil–water loss because thin soils, exposed carbonate bedrock, well-developed epikarst, and strong surface–subsurface connectivity promote both surface erosion and subsurface leakage. Although soil erosion, subsurface leakage, karst rocky desertification, and ecological restoration have been widely studied, the coupling between landscape structural patterns and soil–water loss remains insufficiently synthesized. This semi-systematic critical review synthesizes evidence from karst hydrology, soil erosion, karst rocky desertification, landscape structure, and critical zone studies, with a primary focus on Southwest China. The reviewed evidence indicates that geomorphic setting, land use vegetation structure, bare-rock exposure, and epikarst development jointly regulate runoff generation, infiltration, sediment detachment, subsurface leakage, and sediment connectivity. Peak–cluster depressions commonly favor internal sediment storage and vertical leakage, whereas valley and canyon systems tend to enhance surface runoff connectivity and channelized sediment export. However, pathway dominance varies with rainfall intensity, soil moisture, soil thickness, land use, karst rocky desertification degree, and fracture–conduit connectivity. Long-term soil–water loss may further reshape landscape structure through soil thinning, vegetation degradation, bedrock exposure, and karst rocky desertification feedbacks. Current research is limited by insufficient quantification of subsurface soil loss, weak integration between landscape metrics and hydrological models, and scarce long-term monitoring data. Future studies should integrate field monitoring, tracers, remote sensing, landscape metrics, and coupled surface–subsurface models to support geomorphic-setting-specific karst rocky desertification control. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

Dryland watersheds are governed by tightly coupled source–sink dynamics, in which expanding bare soil and declining vegetated patches amplify runoff, sediment transport, and land degradation. Identifying suitable locations for water harvesting structures remains challenging due to the limited scalability of field assessments and the inability of coarse DEM-based GIS methods to capture critical microtopographic features. This study evaluates whether high-resolution (0.44 m) topographic data, integrated with multi-criteria decision analysis (MCDA), can identify suitable locations for water harvesting structures in dryland watersheds and compares the model discrimination of the Analytical Hierarchy Process (AHP) and the Fuzzy AHP (FAHP). Eight geomorphic and ecological indicators were evaluated and validated using 565 practitioner-identified restoration practice locations across two watersheds in southern New Mexico. The results show that 78% (East Control) and 94% (West Restoration) of validation sites occur within the top two predicted suitability classes, with moderate to good model discrimination (AUC: 0.671–0.723) and strong ranking performance (Boyce Index: 0.945–0.983). AHP and FAHP produced nearly identical outputs (ΔAUC < 1%; ΔBoyce ≤ 0.005). These findings demonstrate that high-resolution topography, coupled with MCDA, provides a robust and transferable framework for the landscape-scale prioritization of nature-based water harvesting structures to support ecohydrological restoration in dryland watersheds. Full article

Soil compaction is a widespread yet insufficiently monitored form of agricultural land degradation, affecting approximately 25% of global soils and nearly 33% of European subsoils, with consequential reductions in soil physical functionality, crop performance, and long-term sustainability; however, approaches for national-scale compaction risk mapping remain limited. A geospatial decision support framework was developed to quantify and map susceptibility to compaction risk across Italy by integrating Earth observation products with multi-criteria decision analysis within a GIS-based Analytic Hierarchy Process. The model combined four indicators: (i) Soil Moisture Index derived from Sentinel 1 C band SAR time series (2018 to 2024), (ii) the Sentinel 2 Normalized Difference Tillage Index, (iii) clay fraction from SoilGrids 2.0, and (iv) an Intensity of Agricultural Practice Index derived from national census statistics. The approach was applied to 74,156 km² of bare soil surfaces across all 20 regions to generate 100 m seasonal and multi-year mean risk maps. Extreme risk (high plus very high) exhibited a bimodal seasonal behavior, occupying 53.6% in winter and 55.5% in autumn, while declining to 24.8% in spring and 26.5% in summer; Southern Italy showed the largest seasonal amplitude (40.7%), and Friuli Venezia Giulia persisted as a hotspot exceeding 50% in all seasons. Comparison with the independent bulk density observations yielded 31.24% accuracy, largely constrained by the temporal mismatch between dynamic processes and static reference data, which represents a constraint of this research. The framework provides an initial screening tool for mapping susceptibility to soil compaction aligned with the EU Soil Strategy 2023 to 2030, supporting targeted interventions by prioritizing spring (March to May) as a low-risk remediation window; however, local conditions must be checked because cultivated crop types are highly diverse, and cropping cycles vary significantly from one species to another. Full article

Built-up area extraction is important for monitoring urban development and land-use change. Index-based methods are widely used for extracting built-up areas from Landsat imagery because of their simplicity and efficiency. However, conventional built-up indices often enhance bare land together with built-up areas due to their similar spectral characteristics, which reduces extraction accuracy and limits automatic threshold selection. To address this problem, this study proposes a built-up area extraction method based on multi-index synthesis and principal component analysis (PCA). First, NDBI (Normalization Differential Building Index), SAVI (Soil-Adjusted Vegetation Index), MNDWI (Modified Normalized Difference Water Index), and the brightness, greenness, and wetness components of the Tasseled Cap transformation were stacked to construct a six-band synthetic index image, enhancing the contrast among built-up areas, bare land, vegetation, and water bodies. PCA was then applied to the synthetic image using both correlation and covariance matrices, and the second principal component was used to enhance built-up area information. The resulting CorPC2 and CovPC2 methods were evaluated and compared with conventional built-up indices. The results showed that both PC2-based methods improved the separability between built-up areas and background features, while CovPC2 achieved the best performance by more effectively suppressing bare-land interference without requiring an additional bare-land mask. In the main experimental area, CovPC2 achieved higher accuracy than the comparison methods, and its Otsu-based result remained close to the optimal-threshold result. Validation in three typical cities further demonstrated the applicability of the proposed method across different Landsat sensors and urban environments. The proposed PC2-based method, particularly CovPC2, provides an effective and more automated approach for Landsat-based built-up area extraction under bare-land interference. Additionally, by using a threshold optimizing algorithm, built-up areas can be automatically extracted with high accuracy. Full article

The Muli mining area on the Qinghai–Tibet Plateau lies within a permafrost region where long-term coal mining has severely degraded native grassland ecosystems. To identify an effective restoration strategy, this study evaluated plant and soil ecological stoichiometry and stoichiometric homeostasis under different combinations of fertilization and seeding rates. A two-factor field experiment was conducted with three fertilization levels (F1–F3) and three seeding rates (S1–S3), using bare slag (BS) and natural grassland (NG) as reference controls. The F3S3 treatment produced the highest aboveground biomass (AGB), representing a 293.55% increase relative to NG. The F2S2 treatment significantly increased plant nitrogen (PN) and phosphorus (PP) contents. In addition, plant carbon-to-nitrogen (PC:PN), carbon-to-phosphorus (PC:PP), and nitrogen-to-phosphorus (PN:PP) ratios under the F2S2, F1S2, and F3S3 treatments, respectively, were closest to those of NG. The PN:PP ratio ranged from 6.05 to 8.20 (<14), indicating that plant growth in the restored plots remained primarily nitrogen-limited. Soil stoichiometric ratios (SOC:TN, SOC:TP, and TN:TP) under the F1S3, F1S1, and F1S2 treatments, respectively, were most similar to those of NG. Principal component analysis (PCA) showed that F3S3 produced the greatest short-term improvement in plant productivity and soil fertility, whereas F2S2 showed the most favorable stoichiometric homeostasis and C:N:P balance relative to natural grassland. Random forest modeling further identified soil total phosphorus, SOC:TN, and available phosphorus as the main factors controlling AGB formation. Overall, F3S3 is suitable for rapid short-term vegetation recovery, whereas F2S2 is more advantageous for long-term restoration when vegetation–soil stoichiometric balance and homeostatic stability are considered. Therefore, restoration projects in similar alpine permafrost mining areas should prioritize the F2S2 treatment to improve both ecological function and system stability. Full article

Environmental changes driven by land use and climate variability profoundly affect basin water balance, yet their separate and combined effects remain poorly understood in data-scarce regions. This study investigates the individual and combined impacts of land use/land cover (LULC) and climate change on seasonal runoff in the Rokel-Seli River Basin (RSRB), Sierra Leone, over two periods (1965–1990 and 1991–2016). Using LULC maps derived from 1988 and 2013 Landsat imagery and the Soil and Water Assessment Tool (SWAT), we simulated hydrological responses under four scenario frameworks. The results reveal a marked expansion of urban, bare, and agricultural land at the expense of forest cover. The SWAT model satisfactorily captured streamflow dynamics during calibration and validation. Land use change alone increased wet-season runoff by 6.55% and decreased dry-season runoff by −13.15%, whereas climate change contributed changes of +24.87% and −31.43%, respectively. A double mass curve analysis and Budyko framework further revealed a regime shift toward higher runoff efficiency (runoff coefficient increased from 0.67 to 0.69), indicating a loss of basin retention capacity. Notably, land use change partially masked the full hydrological deficit induced by climate change, acting as a counter-buffering mechanism. This study provides critical evidence for water resource authorities and local stakeholders to develop adaptive land use and water conservation strategies in data-scarce tropical basins, emphasizing the need to consider both climatic and anthropogenic drivers in seasonal water availability assessments. Full article

Expansive soil slopes are highly susceptible to rainfall-induced shallow failures due to cyclic swelling–shrinkage behavior governed by matric suction variation. This study proposes a composite frame–geosynthetic system (CFGS), comprising a rigid frame integrated with high-performance turf reinforcement mats (HPTRMs), for expansive soil slope protection. The performance of the CFGS was evaluated through geometrically scaled, materially representative physical model tests under repeated wetting–drying cycles and further examined using coupled hydro-mechanical numerical simulations in COMSOL Multiphysics. A bare slope and an HPTRM-protected slope were used for comparison. Under identical laboratory conditions, CFGS reduced cumulative erosion to approximately 13% of that of the bare slope. It also moderated the internal hydraulic response, reducing pore-water pressure fluctuation by approximately 26%, and restrained swelling–shrinkage deformation, with an average deformation attenuation of up to 61%. The numerical simulations showed consistent response trends with the physical model tests, supporting the proposed mechanism of hydraulic regulation, deformation restraint, and stress redistribution. Overall, the results demonstrate the comparative effectiveness of CFGS in mitigating wetting–drying-induced deterioration of expansive soil slopes. Full article