Search Results (212)

Soil moisture (SM) is a core state variable of terrestrial hydrological and land–atmosphere interactions. Machine learning-based downscaling and retrieval frameworks that integrate multi-source remote sensing and auxiliary datasets have become mainstream approaches for generating high-spatial-resolution SM products. However, the spatial domain dependence evolution law governing the relative importance of these multiple predictors remains insufficiently quantified. This study constructs an integrated XGBoost and SHAP interpretability framework to reveal how the contribution and driving mechanisms of predictors shift across spatial domains. We compiled global in-situ SM observations from 24 international soil moisture network (ISMN) monitoring networks spanning 2017–2024. Predictors were classified into five categories: Sentinel-1A radar backscatter, vegetation indices, ERA5-Land meteorological forcing, topographic geospatial variables, and static soil texture attributes. Two modeling approaches were adopted: independent local network models representing the regional domains and a unified composite model representing the global domain. Model performance metrics demonstrate that the global composite model yields robust generalization with minimal overfitting, while individual regional models exhibit domain-specific retrieval differences due to varying land surface conditions. Pearson correlation analyses confirm that physical covariances remain nearly consistent across different domains, whereas cross-category correlations vary with spatial domain and local landscape backgrounds. SHAP-based feature importance quantification reveals clear domain-dependent differences among dominant predictors. This work quantitatively verifies the spatial domain dependence of parameter importance in machine learning-based SM retrieval, providing guidance for domain-adaptive predictor selection and interpretable high-resolution SM modeling under diverse land surface conditions. Full article

Atmospheric particulate matter (PM) is a heterogeneous carrier of toxic metals whose environmental fate and biological effects depend on particle size, source-related composition, chemical form, solubility, and bioaccessibility. Lead (Pb), cadmium (Cd), and nickel (Ni) are of particular concern because atmospheric transport and deposition connect air pollution with persistent contamination of soils, vegetation, waters, sediments, food, and feed, followed by human and animal exposure. This review integrates evidence across a source-to-effect continuum encompassing emission, atmospheric transport, deposition, post-depositional redistribution, food-chain transfer, bioaccessibility, toxicokinetics, molecular toxicity, biomonitoring, remediation, and predictive assessment. Total PM mass and total metal concentration do not adequately represent biologically effective exposure, which is additionally determined by respiratory deposition, gastrointestinal release, dissolution kinetics, absorption, tissue distribution, intracellular retention, and interactions with co-associated constituents. Pb, Cd, and Ni share downstream effects including oxidative imbalance, inflammation, mitochondrial dysfunction, DNA damage, impaired genome maintenance, epigenetic remodeling, and cytogenetic abnormalities, but differ in environmental mobility, persistence, target-organ distribution, and molecular mechanisms. Effective risk assessment therefore requires coordinated multi-matrix monitoring, distinction between total and biologically accessible fractions, pathway-specific remediation, and appropriately validated predictive models. An integrated One Health framework can improve identification of priority matrices, exposure pathways, and risk-reduction measures. Full article

Forest vegetation dynamics are changing under climate change, with important implications for biodiversity conservation, ecosystem services, carbon cycling, and livelihoods. Vegetation regulates terrestrial carbon storage and biophysical feedbacks, but its responses to warming, changing precipitation, rising atmospheric CO₂, and climate extremes are complex and spatially variable. This review presents a PRISMA-guided integrative synthesis of 74 peer-reviewed studies published between 1990 and 2025, incorporating evidence from remote sensing, long-term ecological observations, ecosystem flux measurements, experimental studies, and Earth system modelling. Rather than examining climatic drivers independently, the review synthesizes their interactions with water availability, nutrient limitation, land-use change, disturbance, and biotic processes across physiological, population, ecosystem, and biome scales, with particular emphasis on India. The synthesis reveals that vegetation responses are governed less by individual climatic drivers than by their interactions with resource availability, disturbance, land use, and ecosystem characteristics. Across the reviewed evidence, contrasting greening and browning trends, phenological shifts, species redistribution, and changes in productivity and carbon dynamics were observed. Importantly, greening does not consistently translate into enhanced ecosystem functioning or resilience because increased canopy development may occur alongside water and nutrient limitation, declining carbon-use efficiency, recurrent disturbance, land-use intensification, or changes in species composition. Indian evidence further demonstrates contrasting vegetation trajectories across forests, drylands, Himalayan ecosystems, and agricultural landscapes. Major uncertainties arise from differences among remote-sensing indicators, scale dependency, limited long-term experimental coverage, and incomplete representation of water, nutrient, disturbance, and vegetation processes in coupled climate–vegetation models. Overall, the review identifies a shift from assessing forest vegetation change primarily through greening toward evaluating ecosystem functioning, carbon permanence, and ecosystem resilience capacity as integrated indicators. Full article

Climate change is increasing the frequency and intensity of extreme rainfall events, profoundly affecting vegetation–atmosphere–soil interactions and ecosystem stability. Northeast China (NEC), a major ecological region, is highly sensitive to precipitation variability. However, the annual mechanisms underlying vegetation responses to rainfall extremes, the mediating roles of soil moisture (SM) and vapor pressure deficit (VPD), and the ecosystem-specific differences remain insufficiently understood. This study investigates these processes during 2000–2022 by integrating precipitation extremes, normalized difference vegetation index (NDVI), SM, VPD, and land cover data. Ten rainfall extreme indices were evaluated using the Mann–Kendall (MK) test and Sen’s slope estimator, while NDVI responses were examined through correlation analysis, mixed-effects models, and structural equation modeling (SEM). Results show strong spatial heterogeneity in precipitation extremes, with intensified heavy rainfall in southern NEC and prolonged drought conditions in northern areas. Vegetation exhibited significant greening trends (NDVI slope = 0.0026 yr⁻¹, R² = 0.718, p < 0.001), accompanied by increasing SM (slope = 0.0478 yr⁻¹, p = 0.003) and mild warming (slope = 0.0005 yr⁻¹, p = 0.045). NDVI showed a strong correlation with SM (ρ = 0.65, p < 0.01) but a weak relationship with temperature (ρ = 0.04, p > 0.05), highlighting SM as the dominant driver of regional greening. Grasslands and cultivated lands were more sensitive to rainfall fluctuations, whereas forests showed greater resilience. SEM results indicate that extreme rainfall affects NDVI mainly through indirect pathways mediated by SM and VPD, with mediation effects exceeding 97%. These findings improve understanding of nonlinear vegetation–atmosphere–land interactions and provide scientific insights for climate adaptation, ecosystem management, and ecological restoration under future climate change. Full article

Developing context-specific drought regionalization is crucial for targeted risk management, as drought evolves as a cascading hazard driven by complex land–atmosphere interactions rather than isolated climatic anomalies. However, conventional regionalization frameworks remain largely static and fail to capture the dynamic propagation from meteorological forcing to agricultural impacts. To address this limitation, we developed a framework that links continuous drought dynamics to discrete drought events, enabling identification of propagation patterns and their associated environmental mechanisms across the Loess Plateau, China. By integrating run theory, dimensionality reduction, clustering, and explainable machine learning, we identified three distinct drought propagation regimes: Propagation Blocked, Disaster Amplified, and Response Desensitized zones. At the regional scale, eco-hydrological factors, particularly vegetation productivity and soil moisture dynamics, showed the strongest attribution signals for differentiating drought propagation regimes. However, regime-specific environmental associations differed substantially: (i) propagation blockage was associated with terrain–vegetation interactions; (ii) disaster amplification was associated with low ecological productivity and declining soil moisture; and (iii) response desensitization was associated with intensive agricultural activities and relatively favorable soil moisture conditions, which may partly buffer vegetation responses to thermal and meteorological stress and create apparent resilience that may mask underlying hydrological vulnerability. SHAP analysis further indicated that topography and thermal conditions were strongly associated with broad-scale differentiation, while eco-hydrological conditions showed stronger associations with local regime-specific responses. Anthropogenic activities may also be associated with altered drought propagation pathways and potential risks of unsustainable water use. These findings highlight drought as a dynamic propagation process rather than a static hazard and provide a basis for targeted drought management strategies. Full article

Ecological restoration of abandoned mountainous mines remains challenging because of complex geological conditions, severe substrate degradation, and long-term environmental impacts. Conventional restoration strategies primarily emphasize vegetation establishment and slope stabilization, while the potential contribution of carbon sequestration is often insufficiently considered. Enhanced Rock Weathering (ERW) has recently emerged as a promising negative-emission technology that accelerates the natural weathering of silicate minerals to remove atmospheric CO₂ while improving soil quality. This paper synthesizes current knowledge on ERW and proposes a conceptual framework for integrating this technology into the ecological restoration of abandoned mountainous mines. The framework comprises four complementary dimensions: (1) Physical Restoration, utilizing fragmented rock materials to improve slope stability and substrate structure; (2) Acid–Base Neutralization, exploiting alkaline silicate minerals to alleviate acid mine drainage (AMD) and regulate geochemical conditions; (3) Pedogenic Optimization, promoting soil formation, improving soil physicochemical properties, and facilitating CO₂ infiltration and mineral carbonation; and (4) Biological Synergy, enhancing plant–microbe–mineral interactions to accelerate weathering processes and support long-term ecosystem development. Drawing on existing evidence, we discuss how the use of on-site waste rock as reactive substrates may simultaneously reduce restoration-related carbon emissions, provide essential mineral nutrients (e.g., Ca, Mg, and K), and enhance carbon sequestration potential. Overall, this synthesis suggests that integrating ERW into abandoned mine restoration has the potential to simultaneously advance ecological rehabilitation and climate-change mitigation. The proposed framework provides a theoretical basis for future experimental validation, field-scale implementation, and the development of low-carbon strategies for sustainable mine restoration. Full article

Biogenic volatile organic compounds released from soil (SBVOCs) are an important component of the material exchange and information transmission between terrestrial ecosystems and the atmosphere. Soil ecosystems act as both critical sources and frequently overlooked sinks of BVOCs. SBVOC emissions are mainly regulated by the temperature, moisture, and pH of the soil. Climate warming may enhance volatilization and microbial production in the short term. However, its long-term effects depend on drought, vegetation composition, substrate availability, permafrost thaw, and microbial acclimation. SBVOCs also influence microbial activity, nutrient cycling, plant–microbe interactions, plant defence, and below ground trophic interactions, although the strength of evidence differs among these functions. Ecologically, SBVOCs promote carbon cycling, modulate plant-microbe interactions, and influence atmospheric chemistry. This review further synthesizes SBVOC emission and uptake patterns across different climatic zones. Several challenges remain, particularly the scarcity of long-term quantitative measurements and difficulties in distinguishing multiple emission sources. Our understanding of rhizosphere interactions and climate-change feedback is also limited. It is essential to enhance long-term observational studies and optimize models to deepen our understanding of the role of SBVOCs in the global carbon cycle and air quality. Full article

Understanding microclimatic variability at the land–atmosphere interface is essential for improving knowledge of atmospheric moisture dynamics in heterogeneous mountainous ecosystems. This study analyzes atmospheric relative humidity and soil moisture variability in an experimental Juglans neotropica Diels plantation located in the Ecuadorian Andes under real field conditions. An autonomous photovoltaic-powered monitoring system equipped with low-cost environmental sensors was deployed continuously for 60 days, generating more than 86,000 environmental measurements of atmospheric relative humidity above and below the canopy, together with soil moisture observations. The results revealed persistent vertical humidity stratification associated with canopy structure, characterized by systematically higher atmospheric humidity beneath the canopy compared to the upper atmospheric layer (ΔRH ≈ −36%). Strong intersensor coherence was observed between canopy levels (r = 0.8535), indicating stable temporal consistency in atmospheric variability patterns throughout the monitoring period. Soil moisture exhibited comparatively more stable temporal dynamics than atmospheric humidity, suggesting partial microclimatic decoupling between atmospheric and edaphic layers. The observed humidity gradients remained temporally stable during both daytime and nighttime conditions, supporting the interpretation of canopy-mediated atmospheric buffering processes within the plantation environment. From an ecological perspective, the results indicate that vegetation structure contributes to localized moisture retention, attenuation of short-term atmospheric fluctuations, and regulation of near-surface microclimatic conditions under heterogeneous Andean environmental conditions. Rather than focusing on instrumentation performance, the study provides empirical evidence of persistent canopy-related atmospheric regulation and moisture stratification in a native Andean forest species under continuous field monitoring conditions. These findings contribute to the understanding of land–atmosphere interactions, ecohydrological dynamics, and vegetation-mediated microclimatic regulation in mountainous ecosystems. Full article

Heavy metals are persistent environmental contaminants that accumulate in soils and vegetation, posing significant risks to ecological systems and human health. Pinus nigra needles are widely recognized as effective biomonitors for reflecting spatial and temporal variations in atmospheric heavy metal deposition. However, the complex, nonlinear interactions among multiple pollutants, environmental factors, and site-specific conditions limit the effectiveness of conventional statistical approaches in accurately modeling and predicting contamination patterns. This study investigated the spatial and seasonal distribution of heavy metals in soils and Pinus nigra needles across different environmental settings in Adıyaman, Türkiye, including urban traffic zones, an organized industrial area, a cement factory vicinity, and a clean reference site. Metal concentrations were determined using inductively coupled plasma mass spectrometry (ICP–MS) following standardized acid digestion procedures. To address the limitations of traditional methods and capture complex nonlinear relationships, advanced machine learning (ML) algorithms—multilayer perceptron, Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting—were employed to model elevation based on heavy metal concentrations. The dataset was divided into training (80%) and testing (20%) subsets, and model performance was evaluated using R², RMSE, MAE, MAPE, and EVS. Among the models, XGBoost exhibited superior predictive performance. Excluding Cd, Cr, and Cu, it achieved R² = 0.9996 (RMSE = 0.068) in training and R² = 0.9526 (RMSE = 17.77) in testing. Including these metals further improved performance to R² = 0.9999 (RMSE = 0.054) for training and R² = 0.9890 (RMSE = 5.55) for testing. The results confirm that Pinus nigra needles are reliable bioindicators of heavy metal accumulation. More importantly, the integration of biomonitoring data with ML techniques provides a powerful framework for capturing complex environmental interactions and improving predictive accuracy, thereby supporting more effective environmental monitoring, risk assessment, and sustainable management strategies. Full article

The intensification of heat waves and the increase in average temperatures, particularly evident in the Mediterranean context, underline the urgency of strengthening sustainable spatial planning, introducing concrete tools to understand and manage how urban form, land-use and local microclimate interact with each other. In this direction, the paper proposes an integrated methodological path to map territorial exposure to surface heat stress in densely urbanised contexts, applying it to the case study of the Caserta conurbation. The approach uses several levels of analysis: (i) the estimation of the Normalised Difference Vegetation Index (NDVI) and the calculation of Land Surface Temperature (LST) from the Landsat series (1987–2022), using radiance/reflectance measurements and deriving LST from the Top-of-Atmosphere Brightness Temperature (BT) and from the Land Surface Emissivity (LSE); (ii) the consistency check of satellite-derived surface temperature using meteorological station data from the archive of the Multi-Risk Functional Center of the Civil Protection of the Campania Region, fed by the measurements of the sensors installed in the area by the Campania Regional Environmental Protection Agency (ARPAC); (iii) the evaluation of the intensity of the Surface Urban Heat Island (SUHI) as the difference between LST values extracted from paired urban and non-urbanised reference areas; (iv) the verification of the relationship between NDVI and LST by linear regression, with NDVI as the explanatory variable and LST as the dependent variable, showing a statistically consistent inverse relationship. The results support a multi-scalar reading of the intervention priorities, highlighting that the potential relevance of nature-based solutions (NBS) and cooling strategies varies according to local urban morphology, geographical configuration, and degree of soil sealing, requiring context-specific planning evaluations. Full article

Global warming is reshaping terrestrial water cycling and near-surface climate risks through atmospheric moistening, enhanced precipitation variability, rising evaporative demand, and more frequent compound extremes. This narrative review synthesizes recent advances in land–atmosphere interactions and atmospheric water cycle feedbacks, and its distinctive contribution is to connect physical feedback chains with human land surface perturbations, compound risk, and observation model machine learning evidence. We reviewed the literature from Web of Science, Scopus, Google Scholar, publisher databases, and Crossref metadata, prioritizing peer-reviewed studies published mainly during 2010–2026 while retaining foundational work on soil moisture feedbacks, moisture recycling, irrigation, aerosols, and boundary-layer processes. The synthesis emphasizes where evidence is robust, where feedback signs are regime dependent, and where uncertainty still propagates from evapotranspiration partitioning, boundary-layer diagnosis, aerosol–cloud interactions, human water management, and nonstationary climate conditions. The review concludes that the same land surface perturbation may cool locally, increase humid heat exposure, alter downwind precipitation, or intensify water depletion, depending on the climate regime, season, scale, and management. Future research should therefore move beyond single-variable correlation analyses toward causal, cross-scale, and risk-oriented attribution frameworks that integrate multi-source observations, process models, moisture tracking, and physically constrained machine learning. Full article

The rapid expansion of urbanization in recent decades has intensified the urban heat island effect, driven by reduced vegetation cover, widespread use of heat-absorbing materials, and increases in surface and atmospheric temperature that may reach 5–6 °C. These conditions negatively impact well-being, quality of life, and human health. In response, numerous studies have examined mitigation strategies based on high-albedo materials and urban vegetation. This systematic review analyzes 225 peer-reviewed articles published between 2016 and 2025 addressing urban heat mitigation, surface thermal conditions, urban vegetation, outdoor thermal comfort and microclimate simulations. It provides a comprehensive synthesis, highlighting key findings and implications for future research. According to the Köppen–Geiger classification, most studies were conducted in humid subtropical and warm Mediterranean climates. The analysis focuses on urban canyon interventions, where vegetation is primarily modeled as shading trees (79.2%), along with other forms such as grass or shrubs (27.1%), mainly during the summer season. Results indicate that integrated mitigation strategies combining vegetation and high-albedo surfaces (≈0.8) generally provide greater cooling benefits than isolated interventions. Overall, the findings underscore the importance of the interaction between vegetation shading and surface properties for mitigating urban heat in outdoor spaces. Full article

Mountain ecosystems are sensitive response units and critical ecological barriers to global climate change. Located in the mid-latitude climate transition zone, these ecosystems feature high ecological sensitivity and complex driving mechanisms, creating an urgent need to conduct long-sequence, high-precision dynamic assessments in order to support ecological conservation and climate adaptation decision-making. However, three key research gaps remain in the field: first, traditional assessments are dominated by static observation, lacking the capacity for long-sequence dynamic analysis and future projection; second, the coupled interaction mechanism among multiple ecological factors remains unclear, with insufficient quantitative and physical mechanism characterization; third, existing ecological zoning has not been validated for robustness, rendering it incapable of addressing climate disturbances and extreme scenarios. In order to study the regional atmospheric ecosystem, this study takes Shangluo in the eastern Qinling Mountains as the study area and constructs an integrated assessment framework integrating multi-dimensional diagnosis, simulation and projection, dynamic zoning and robustness validation based on long-sequence multi-factor data covering the years 1965–2024. The study aims to reveal the long-sequence evolution patterns and four-dimensional coupling mechanism of the Qinling Mountains atmospheric ecosystem, developing a reproducible and transferable dynamic assessment model. The results show that the study area exhibits the characteristic of elevation-dependent warming, and the correlation coefficients between elevation and air temperature, and between vegetation coverage and air quality reach −0.89 and −0.76, respectively.; ecological quality presents a spatial pattern of being high in the southwest and low in the northeast, with a coefficient of variation across the whole study area lower than 0.03. The results of 1000 Monte Carlo random disturbance validation runs show that even under intensified climate stress, the zoning pattern still maintains extremely strong disturbance resistance. This study reveals the steady-state multi-factor interaction mechanism in mountainous regions, addressing the defects of traditional static assessments that ignore ecosystem evolution and lag effects. The dynamic projection model constructed in this study can be transferred to similar mid-latitude mountainous regions worldwide, providing theoretical and technical support for regional ecological governance. Full article

Mountain vegetation in dryland regions is highly sensitive to climatic variability, particularly changes in water availability and atmospheric demand. This study assessed the relationships between vegetation coverage and climatic factors in the Chinese Altay Mountains from 2000 to 2024 using MODIS NDVI data, meteorological observations, drought indices, and extreme climate indicators. Pixel-based correlation analysis and directional interaction classification were used to evaluate the spatial consistency and divergence between vegetation dynamics and climate variability. The results showed that water availability was the dominant factor controlling vegetation cover. Annual precipitation, SPEI, and precipitation-related extreme indices were generally positively associated with vegetation coverage, whereas warmth-related indices such as GSL, WSDI, and TX90 were mostly negatively associated with vegetation coverage. Temperature showed a spatially variable effect, with warming tending to suppress vegetation in water-limited low- and middle-elevation areas but potentially benefiting vegetation in cold-limited high-elevation zones. SPEI showed a more consistent relationship with vegetation coverage than TVDI, indicating that cumulative climatic water balance better captured regional vegetation drought responses than surface dryness alone. These findings highlight the importance of vegetation–atmosphere water deficit in regulating mountain vegetation dynamics and provide a scientific basis for ecological conservation and water resource management in the Altay Mountains. Full article

Microbial functional traits play a central role in regulating carbon mineralization efficiency (CME) in peatlands, yet how they respond to concurrent warming and atmospheric nitrogen deposition remains unclear. In this study, peat soils from three vegetation types (sedge, reed, and shrub) were subjected to controlled microcosm incubations simulating warming and nitrogen addition gradients. Microbial community composition and functional profiles were characterized using 16S rRNA high-throughput sequencing and Functional Annotation of Prokaryotic Taxa (FAPROTAX) functional prediction, while dissolved organic matter (DOM) composition was analyzed via excitation–emission matrix fluorescence spectroscopy with parallel factor analysis (EEM-PARAFAC) and fluorescence indices. Integrating correlation analysis, Random Forest, and partial least squares path modeling (PLS-PM) modeling, we identified microbial functional traits as key factors linking environmental changes to soil CME, with DOM serving as a substrate-mediated pathway. External nitrogen input primarily drove shifts in microbial functional composition, whereas warming modulated substrate utilization preferences and DOM turnover. The interaction between warming and nitrogen selectively reshaped microbial functional profiles, thereby jointly determining CME. Functional traits explained more variation in CME than taxonomic composition, indicating a “structure–function decoupling” under environmental change. These findings highlight the central role of microbial functional traits in peatland carbon transformation and suggest that the net response of peatland carbon emissions to future environmental change will depend critically on the balance between warming magnitude and nitrogen deposition levels. Full article