Search Results (338)

Climate-induced drought and intensifying land-use pressures threaten ecosystem services and agricultural productivity, particularly in regions with distinctive soil and ecological characteristics. Alabama’s Black Belt, defined by its clay-rich soils and shaped by a legacy of plantation agriculture, uneven land tenure, and persistent socioeconomic disadvantage, is increasingly vulnerable to these interacting stressors. This study analyzes long-term (2000–2023) spatiotemporal patterns of Land Use Land Cover (LULC) change and vegetation response to drought to inform sustainable resource management. Multi-temporal Landsat imagery and National Land Cover Database (NLCD) products were used to quantify LULC dynamics. At the same time, vegetation condition and moisture stress were assessed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI). Drought conditions were evaluated using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), which incorporates temperature-driven evaporative demand. Results indicate substantial landscape change, including declines in deciduous forest (−17.78%) and pasture/hay (−13.17%), alongside increases in medium-intensity developed land (+20.25%) and evergreen forest (+10.62%). Declining NDVI and NDMI values indicate increasing vegetation stress, particularly during prolonged droughts. Vegetation response exhibited a weak relationship with SPI (R = 0.37) but a stronger association with SPEI (R = 0.59), underscoring the importance of accounting for atmospheric water demand. These findings highlight the growing vulnerability of Black Belt ecosystems to coupled climate and land-use pressures and provide insights to strengthen climate-resilient agricultural management. Full article

This review investigates the multifaceted roles of nitrogen-fixing and phosphate-mobilizing bacteria in natural ecosystems, with a particular focus on their contributions to plant growth and sustainable soil management. These microbial communities contribute substantially to nutrient cycling by converting atmospheric nitrogen into plant-available forms and mobilizing insoluble phosphorus in soil, thereby enhancing soil fertility and promoting sustainable plant productivity. This review synthesizes current knowledge on the mechanisms underlying biological nitrogen fixation, phosphate solubilization and mineralization, and the production of plant growth–promoting metabolites. Particular attention is given to plant–microbe interactions and their role in improving nutrient availability, regulating plant physiological processes, and enhancing tolerance to abiotic stresses such as salinity, drought, and heavy metal contamination. The findings underscore the ecological importance of these plant-associated microbial communities and highlight their potential applications in biofertilizer and biostimulant development for sustainable agriculture and reduced dependence on synthetic fertilizers. Full article

Soil moisture (SM) in the mountains is critical for agropastoral productivity, and it is subject to both large-scale climate gradients and fine-scale effects of terrain, vegetation and soil. However, how the climate, topography, soil and vegetation factors impact surface SM spatiotemporal dynamics remains elusive in mountainous terrains, due to their complex interactions. Based on multi-source datasets, this study employs the structural equation model to investigate the impact pathways of climate and vegetation factors on annual surface SM dynamics from the year 2000 to 2022 in the Tianshan Mountains of China (TS). We also utilize the factor and interaction detectors of Geographical Detector to explore the individual and interactive effects of climate, topography, soil and vegetation factors on the spatial pattern of the annual surface SM. Moreover, their integrated impacts on the spatiotemporal dynamics of annual surface SM were investigated based on the explanatory power from the factor detector and total effects from structural equation modeling. The results showed that the multi-year average surface SM was 0.21 m³·m⁻³ for the whole region, with greater values in areas with dense vegetation and high elevation. Annual surface SM exhibited significant increasing trends across different land cover classifications and elevation zones, which was directly influenced by vegetation greenness enhancement. Precipitation (PRE) and relative humidity (RH) also significantly influenced the temporal variations in surface SM through their indirect effect on vegetation greenness, while these indirect effects were much lower than the direct effect of vegetation greenness. RH, PRE and surface net solar radiation (SSR) showed strong individual and interactive effects on the spatial distribution of surface SM, particularly the interactive effects of RH and PRE with wind speed (WS). Surface SM was highly sensitive to RH and PRE in the central TS. Overall, vegetation greenness, PRE and RH were the main drivers of surface SM variations across both temporal and spatial scales, while SSR, total evaporation and WS primarily shaped its spatial distribution. These insights enhance our understanding of land–atmosphere interactions in mountainous areas and provide scientific references for sustainable agropastoral water resource management under global warming. Full article

Large-scale photovoltaic (PV) deployment in arid deserts may alter land–atmosphere interactions and influence groundwater systems, yet such impacts remain poorly quantified due to limited high-resolution observations. To overcome the coarse spatial resolution of GRACE data, this study develops a CNN-LSTM-Attention deep learning framework to downscale terrestrial water storage anomalies (TWSA) from 0.25° × 0.25° to 0.1° × 0.1° over the Huadian PV base in the Tengger Desert, China, during 2004–2024. Groundwater storage anomalies (GWSA) were derived using a water-balance approach, and piecewise linear regression was applied to detect trend shifts associated with PV development. Results show a persistent decline in TWSA and GWSA before 2022, followed by short-term recovery signals afterward. Groundwater responses exhibit greater magnitude and delayed behavior relative to soil moisture. Spatial analysis reveals stronger variability and more frequent deficits in the western subregion, indicating intra-base heterogeneity. A seasonal phase analysis identifies an approximately six-month lag between soil moisture and groundwater, highlighting constraints from deep vadose-zone processes. The findings suggest that groundwater dynamics reflect the combined effects of climate variability, infiltration lag, and PV-related land surface modification rather than a single driver. This study demonstrates the potential of deep-learning-based GRACE downscaling for groundwater monitoring in human-modified arid regions and provides insights for sustainable water management under renewable energy development. Full article

Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha⁻¹. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions. Full article

Drought is a highly destructive natural disaster that inflicts severe economic losses. Its formation mechanisms are complex, yet existing studies have often focused on single driving factors, leaving the synergistic effects of multiple factors insufficiently explored. Based on multi-source data from Xinjiang spanning 1981–2020, this study systematically examined the combined impacts of atmospheric circulation, underlying surface conditions, and human activities on drought, using the multi-temporal-scale Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI), along with partial correlation analysis, spatial autocorrelation, and principal component analysis. The results show that Xinjiang experienced a pronounced drying trend over the past 40 years, with the seasonal SPEI and SSI both exhibiting significant declines. Drought intensity was higher in northern Xinjiang than in the south. Correlations between drought indices and circulation indices, such as Atlantic Multidecadal Oscillation (AMO), were relatively weak, indicating a limited regulatory influence of large-scale circulation on regional drought under the dual constraints of topography and an inland setting. Among underlying surface factors, slope significantly influenced drought spatial patterns. Mountainous areas and basin interiors showed positive spatial correlations, characterized respectively by high–high clustering (high slope and high drought index) and low–low clustering (low slope and low drought index). In contrast, basin margins exhibited low–high clustering (low slope surrounded by high drought index), reflecting negative spatial correlation. Aspect showed no significant effect. Vegetation cover displayed clear seasonal coupling with drought, with strong negative correlations in spring due to intensified water stress. Human activities also played a prominent role. Since the mid-1990s, the expansion of built-up land and increased agricultural water use have shifted drought–land use relationships toward low–high clustering (low drought index surrounded by high land-use intensity) in southern Xinjiang oases, and toward low–low clustering (low drought index and low land-use intensity) in eastern Xinjiang. Meanwhile, ecological restoration projects promoted a transition from low–high to high–high clustering (high drought index and high land-use intensity) in some areas, alleviating local drying trends. Principal component analysis further revealed a shift in the dominant driver: land-use change was the primary factor before 2005, whereas vegetation cover became the key driver thereafter. By clarifying the mechanisms underlying multi-factor interactions in drought in Xinjiang, this study provides scientific support for integrated water resource management, ecological conservation, and climate adaptation strategies in arid regions. Full article

Microplastics (MPs) and nanoplastics (NPs) have emerged as pervasive contaminants across diverse environments—including soil, water, and the atmosphere—posing substantial risks to resident organisms. Concurrently, alien plant invasion represents a significant driver of environmental change, introducing considerable ecological risks to terrestrial ecosystems. Synthesizing evidence from 26 original research articles, this review examines the bidirectional interactions between micro(nano)plastics (MNPs) and terrestrial invasive plants. A growing body of evidence indicates that MNPs alter the growth and performance of both invasive and native plants. In most documented cases, MNPs appear to enhance the competitive ability of invasive plants, thereby elevating their invasion potential. However, counterexamples exist wherein MNPs strengthen the competitiveness of native plants, consequently mitigating invasion risk. These divergent outcomes are likely attributable to a suite of influencing factors, notably the characteristics of the MNPs (e.g., type, size, concentration), the specific invasive and native plant species involved, and variations in experimental conditions. Key mechanistic pathways involve MNPs-induced disturbances in soil microecology—particularly nutrient dynamics and rhizosphere microbiomes—and allelopathic interactions. Conversely, invasive plants may adsorb/absorb MNPs and subsequently modify their environmental fate and behaviors (e.g., degradation, transport). Finally, we delineate critical knowledge gaps and propose prioritized directions for future research. This review advances our understanding of the ecological risks associated with plant invasions in an era of pervasive MNP pollution and offers a scientific foundation for developing informed management strategies. Full article

Southern Africa is highly sensitive to climate variability associated with the El Niño Southern Oscillation (ENSO), which strongly influences hydroclimate, vegetation dynamics, and atmospheric composition. This study examined the impacts of the 2015/16 El Niño on vegetation, meteorological conditions, and atmospheric emissions over Southern Africa using satellite observations and reanalysis data. Time-lagged cross-correlation analysis of seasonally adjusted time-series was applied to characterize synchronous and delayed interactions among vegetation indices, hydrological variables, meteorological drivers, and air-quality parameters. Bayesian causal impact analysis was further used to quantify El Niño-induced anomalies by comparing observed conditions with counterfactual scenarios representing the absence of the event. The results showed that vegetation greenness responds primarily to concurrent moisture availability, with strong positive associations between NDVI, precipitation, soil moisture, and canopy water. Moisture-related variables exert delayed influences on atmospheric composition, highlighting the role of wet scavenging and dilution. Carbonaceous aerosols (black carbon [BC] and organic carbon [OC]), particulate matter [PM_2.5], and aerosol optical depth exhibit strong synchronous coupling, indicating a dominant biomass-burning source. The causal impact analysis reveals statistically significant and sustained post-2015 increases in fire-related emissions (carbon monoxide [CO], BC, OC, PM_2.5, and aerosol optical depth [AOD]), particularly during austral winter and dry seasons. In contrast, precipitation, soil moisture, evapotranspiration, and vegetation greenness show persistent negative anomalies, reflecting widespread drought stress under elevated temperatures. Overall, the findings demonstrate that the 2015/16 El Niño amplified fire emissions while suppressing ecosystem functioning across Southern Africa, underscoring strong climate–fire–vegetation feedback with important air-quality and environmental implications. Full article

Flooding is one of the major natural disasters that have a major impact on urban areas due to the increasing intensity of factors like extreme weather conditions, climate change, and unplanned urbanization. Considering Cook County, Illinois, the rapid development of the region, flat topography, and the induced rainfall extremes from climate change increase the potential risk of flooding when interacting with dense urban exposure and infrastructure. This study employed the Frequency Ratio (FR) model in a GIS environment to create a high-resolution flood susceptibility map of the county. The map was developed using 281 historical flood points collected from several authoritative sources, such as National Oceanic and Atmospheric Administration (NOAA) Storm Events Database records, Federal Emergency Management Agency (FEMA) Flood Insurance Study (FIS) and Flood Insurance Rate Map (FIRM)-based FIRMette products, and U.S. Geological Survey (USGS) flood-inundation studies. Thirteen conditioning factors, including land use, elevation, slope, soil drainage, rainfall, and distance to the stream, were used to calculate FR values and to develop the Flood Susceptibility Index (FSI). The resulting FSI was grouped into four susceptibility zones: low, medium, high, and very high. The findings indicated that more than 64% of Cook County has a high and very high risk of flood susceptibility, particularly in the vicinity of major river corridors. The model was validated using testing data with a 91.4% prediction accuracy, which also demonstrated the reliability and applicability of the FR model in the urban flood susceptibility assessment. The map serves as a valuable tool for risk-based urban planning and design of flood mitigation infrastructure in one of the most populated counties in the United States. Full article

As a key parameter of the Earth’s ecosystem, soil moisture significantly influences land-atmosphere interactions and has important applications in meteorology, hydrology, and agricultural studies. However, existing passive microwave remote sensing products of soil moisture are limited by their discontinuous temporal coverage and relatively coarse spatial resolution (typically 25–55 km), which cannot meet the requirements for fine-scale applications. This study developed and compared four machine learning-based downscaling approaches to improve the spatiotemporal resolution of MCCA SMAP soil moisture products. The methodology involved establishing complex nonlinear relationships between soil moisture and various high-resolution surface parameters including albedo, evapotranspiration, precipitation, and soil properties. High-resolution soil moisture maps were generated by leveraging the scale-invariant characteristics between soil moisture and surface parameters, followed by comprehensive evaluation using in situ ground observations and triple collocation analysis. The results demonstrated that all downscaling models showed excellent consistency with original MCCA SMAP observations (R > 0.93, RMSE < 0.033 m³ m⁻³), while successfully providing enhanced spatial details. The Random Forest (RF) model exhibited superior performance, showing higher correlation coefficients and lower biases when compared with in situ measurements. Uncertainty analysis revealed relatively low uncertainty levels for all models except Backpropagation Neural Network (BPNN) model. The RF-downscaled products accurately tracked temporal variations of soil moisture and showed good responsiveness to precipitation patterns, demonstrating their potential for fine-scale hydrological applications and regional environmental monitoring. Full article

Soil erosion control is an important aspect of promoting ecological civilization and a key support mechanism for achieving the ‘dual-carbon’ goals. The successful implementation of PPP (Public–Private Partnership) projects for soil erosion control requires widespread participation from farmers. Therefore, it is necessary to study the evolutionary mechanisms of farmer participation behavior and the process of their state transformation, as well as exploring how to enhance farmers’ participation willingness. First, a dynamic group model of farmers’ participation behavior was constructed by dividing them into five states: unknown, observing, participating, rejecting, and immune. Then, the strategic interactions between the government, social capital, and farmers under the PPP model were considered, and this was coupled with the dynamic group model. Finally, Chongqing City was taken as a typical case for numerical simulation to analyze the evolutionary patterns of farmers participation behavior. The results indicate that: (1) synergistic effective government regulation and active enterprise governance can elevate the farmer participation rate to approximately 71% and facilitate the convergence of the system toward a stable high-participation equilibrium; (2) government subsidies need to be controlled within a reasonable range to ensure policy effectiveness; (3) improving government publicity and enhancing the social atmosphere can increase farmers’ participation rate to approximately 71% and 78%, respectively, significantly boosting their willingness to participate; (4) improving the social security system and reducing perceived risks can help increase farmers’ participation rate. The research conclusions can provide a valuable reference for local governments in China in formulating soil erosion control policies. Full article

Soil microbes are central to carbon and nutrient cycling; however, the influence of tillage practices on plant–soil microbe interactions, particularly their contribution to carbon stabilization under increasing atmospheric CO₂, remains insufficiently understood. This systematic review evaluated 238 studies published between 2010 and 2025 from Scopus, Web of Science (WoS), and Google Scholar, of which 113 met the inclusion criteria related to carbon dynamics, agro-climatic conditions, and soil–microbial processes. Evidence indicates that conventional plowing (CP) disrupts microbial structure, habitat, and function, resulting in lower soil organic carbon (SOC) stocks and elevated CO₂ emissions. Conversely, conservation tillage promotes rhizodeposition, microbial biomass carbon (MBC) accumulation, and enhanced nitrogen (N) and phosphorus (P) availability, thereby increasing SOC sequestration and reducing CO₂ emissions. Overall, insights from this study will enhance our understanding of beneficial microbes that enhance carbon stabilization and root exudate compounds, which trigger specifically needed nutrients in the rhizosphere. Full article

Clarifying the interplay between extreme soil moisture (SM) and precipitation (P) is imperative to understand the impacts of extreme events on ecosystems in a changing climate. However, the detailed relationships, pathways, and quantitative characterization of SM-P extremes at a quasi-global (50° S–50° N) scale remain unclear. Here, we systematically evaluated the co-occurrence and temporal dependencies of SM-P extremes from 2000 to 2022, quantified their synchronous probability, used statistical modeling to reveal the directional pathways among evapotranspiration (ET), P, and SM, and detected long-term trends in P and SM extremes. Our results show a significant increase in the co-occurrence frequency of SM-P extremes globally, with strong spatiotemporal co-occurrence patterns. A lower conditional probability (62%) of extreme SM anomalies was observed within a short term (34 days) after P extremes occurred, while a significantly higher conditional probability (88%) of P extremes was found following extreme SM anomalies. Path analysis (structural equation modeling) indicates a strong direct positive pathway from P to SM, whereas SM influences P indirectly through ET. Compared to satellite-based observations, the BCC-ESM1 model within the CMIP6 framework reproduces the synchrony of SM-P extremes reasonably well, offering a feasible alternative for predicting SM-P relationships in regions lacking satellite observations and aiding future projections of their trends. Our study broadens the perspective on land–atmosphere interactions and coupling mechanisms, providing a solid theoretical basis for predicting and managing the effects of extreme events on ecosystems. Full article

Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring. Full article

Climate warming impacts arctic and subarctic lands, subjecting it to a generalized rise in soil temperature and causing changes in the surface cover. Land cover is a key control parameter for soil hydrothermal states, and its study by satellite imagery is necessary for monitoring boreal surface changes over time at large scales. Understanding the links between land cover and environmental conditions is also crucial to anticipate the impacts of atmospheric changes on continental surfaces. Sentinel-1 and Sentinel-2 data combined with a field campaign in July 2024 were used to produce a 10 m spatial resolution land cover map in the Abisko region, northern Sweden, covering 2180 km² and including three watersheds with an overall accuracy exceeding 94%. In parallel, temperature and precipitation fields were statistically downscaled at 100 m spatial resolution using topography, ordinary kriging based on weather stations and reanalysis. The relationships between surface areas and average summer temperature–precipitation clusters reveal that the vegetation distribution closely reflects the recent atmospheric conditions with the treeline following the 10.2 °C July–August isotherm in the considered area. This study provides a spatial basis for investigating the complex atmosphere–surface interactions and for assessing the sensitivity of boreal landscapes to ongoing climate warming. Full article