The frequent conversion between cropland and grassland in agropastoral ecotones poses severe challenges to the protection of grassland biodiversity, and a systematic understanding of the relationship between these two aspects is urgently needed. In this study, the West Liaohe River Basin, which is a typical agropastoral ecotone in northern China, was chosen as an example. Using the field investigation data from 2023 and 2024, we calculated various biodiversity indices at both α and β scales for grassland vegetation and soil bacteria, and then analyzed the effects of the interactions at the cropland-grassland interface on grassland above-ground biodiversity, below-ground biodiversity, and their interrelationships. Moreover, we explored the driving factors of grassland biodiversity at the cropland-grassland interface. Notably, interactions at the cropland-grassland interface adversely affected grassland above- and below-ground biodiversity. Compared with the sampling points that were farther from the cropland-grassland interface (25 and 50 m), the sampling points located very close to the interface (5 and 10 m) had a decrease in species richness of more than 5.00%. This effect was jointly determined by various vegetation and soil attribute indicators and the regional environment. The litter and soil organic carbon played a prominent role in modulating the relationships between grassland above- and below-ground biodiversity at the cropland-grassland interface. The results suggested that intensive management of cropland and grassland should be enhanced in areas where agriculture and animal husbandry alternate, the disorderly reclamation and random abandonment of cropland should be prohibited, and the policy of returning cropland to grassland should be promoted systematically. These findings could provide reference data for related studies and promote the protection of grassland biodiversity in agropastoral ecotones.

Accurate estimation of gross primary production (GPP) is crucial for understanding terrestrial carbon cycling, yet the regional performance of existing GPP products remains insufficiently quantified. This study evaluated four widely used GPP products, i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS, e.g., MOD17), Global OCO-2-based Solar-Induced Chlorophyll Fluorescence (SIF) product (GOSIF), Global Land Surface Satellite (GLASS), and Penman-Monteith-Leuning Version 2 (PML_V2), across five representative ecosystems in the Heihe River Basin (HRB), northwestern China using 18 eddy covariance (EC) sites during 2007-2022. Multi-scale validation revealed pronounced spatial and ecosystem-dependent differences. Although all products captured the general basin-scale gradient, an analysis of the spatial coefficient of variation (CV) revealed distinct differences in their ability to resolve spatial heterogeneity: PML_V2 and GLASS reasonably captured the observed spatial variability, whereas MOD17 and GOSIF tended to smooth over fine-scale details. Furthermore, a systematic compression of the productivity gradient was evident across products, characterized by considerable underestimation in high-productivity ecosystems (forest land and cropland) and general overestimation in grassland. Temporally, all products performed more reliably in capturing seasonal dynamics than in reproducing inter-annual variations. At the growing season scale, GLASS and GOSIF achieved the highest explanatory power (r>0.95 at several sites), whereas MOD17 exhibited the lowest error (root mean square error (RMSE)=25.26 g C/m² at the Jingyangling site (JYL)). However, inter-annual performance declined markedly, with MOD17 showing weak correlations (r<0.30) at most sites. Ecosystem-specific results identified GOSIF as superior for cropland and wetland ecosystems, while PML_V2 offered the best performance in grassland and desert ecosystems by minimizing systematic bias. Notably, all products consistently failed to establish meaningful correlations (r<0.21) with observations in desert areas due to the low ratios of signal to noise. Consequently, we recommend an ecosystem-dependent application strategy—specifically prioritizing GOSIF for cropland and wetland and PML_V2 for grassland—and urge extreme caution when applying single remote-sensing GPP products in arid desert areas.

Remote sensing-based soil salinity inversion serves as a crucial approach for monitoring and assessment in arid regions. However, most existing models rarely account for the spatial autocorrelation (SAC) of soil salinity, which limits both their predictive accuracy and ability to capture spatial patterns. To address this gap, this study investigated the Minqin Oasis and its adjacent desert-oasis transition zone in Northwest China. Based on collected field soil samples and concurrently acquired Landsat-8 OLI remote sensing images in 2024, we incorporated characteristic bands reflecting SAC into conventional spectral indices. Through multi-band combination optimization and comparison of different models' predictive performance, we constructed an optimal soil salinity inversion model for the Minqin Oasis and its adjacent desert-oasis transition zone. The results demonstrated that incorporating SAC of soil salinity markedly improved model performance, with the Gradient Boosting Regression Trees (GBRT) model incorporating SAC (GBRT_SAC) achieving the best accuracy. Compared with the traditional spectral index-based GBRT model, the coefficient of determination (R²) increased by 7.320%, the root mean square error (RMSE) decreased by 20.230%, and the mean absolute percentage error (MAPE) decreased by 121.01% using the GBRT_SAC model. The soil salinity distribution derived from the GBRT_SAC model revealed pronounced spatial heterogeneity, with salinized areas covering approximately 1256.75 km² (36.170% of the total area). Soil salinity was jointly influenced by natural and anthropogenic factors. At the regional scale, soil type and vegetation type emerged as the dominant drivers shaping soil salinity patterns. In contrast, within the oasis interior, soil salinity was primarily driven by groundwater table regulated by irrigation, leading to surface salt accumulation through capillary rise. In the 1000 m desert-oasis transition zone, the explanatory power (q-value) of all environmental factors for spatial variation of soil salinity significantly increased, indicating a sensitive interface where hydrological and aeolian processes interact. Notably, although soil salinity was relatively lower in sandy areas, sand content emerged as the most influential factor in this region (q-value=0.483), effectively serving as a key indicator of the transitional environment. By introducing SAC-based features into soil salinity inversion models, this study provides a robust methodological framework and valuable data to support understanding and management of soil salinization in arid desert-oasis ecotone systems.

Nebkhas are important aeolian landforms in arid regions and have notable windbreak and sand-fixing functions. However, the effects of shrub type and developmental stage on nebkha performance in high-wind energy regions remain insufficiently understood. This study quantified the windbreak and sand-fixing effects of three typical nebkha types (i.e., nebkhas formed by Reaumuria soongorica, Caragana tibetica, and Nitraria tangutorum) at different developmental stages (i.e., initial, developing, stable, and activated stages) in the Yabrai Mountain aeolian corridor, a high-wind energy region in China. Field sampling was carried out in three subregions along the prevailing wind direction (upwind, central sand-transport, and downwind) in March and November 2025. Particle-size analysis was conducted to characterize the differentiation of surface aeolian sediments, and field measurements combined with numerical simulations were used to analyze wind-speed profiles, windbreak efficiency, and airflow structure. The results showed that surface sediments on the windward slope and mound crest were dominated by clay and silt, whereas higher sand fractions occurred on the leeward and lateral slopes. The mean particle size followed the order: N. tangutorum (2.27 Φ)<C. tibetica (2.30 Φ)<R. soongorica (2.37 Φ). Windbreak and sand-fixing effects ranked as stable stage>activated stage>developing stage>initial stage, with N. tangutorum nebkhas showing the strongest performance. When airflow passed over the nebkhas, wind speed increased above the canopy and along the lateral slopes; at the same time, a pronounced leeward deceleration zone formed within normalized horizontal distance (x/L) of 0-6 and relative height (z/H) of 0.0-0.5, where x is the horizontal distance, z is the vertical height, and L and H denote the shrub canopy major axis and shrub canopy height, respectively. Wind speed increased with z/H and x/L in the leeward direction and approached the reference wind speed over bare ground at z/H>1.2. Similar airflow patterns were observed among nebkha types. Prioritising N. tangutorum, combined with C. tibetica, and maintaining suitable developmental stages can enhance windbreak and sand-fixing performance in high-wind energy regions. These findings provide a scientific reference for constructing stable and efficient ecological protection systems in extreme wind environments, and support sand-control planning and vegetation configuration in arid and semi-arid areas.

Soil organic carbon (SOC) stocks play a critical role in maintaining soil fertility, regulating biogeochemical cycles, and mitigating climate change, yet they are increasingly threatened in semi-arid agricultural regions. This study quantified spatiotemporal changes in SOC concentration and stocks across Hamadan Province, western Iran, over a 16-a period (2010-2025), a region that has undergone rapid agricultural intensification. A total of 212 soil samples were collected from the same georeferenced locations during both sampling campaigns, targeting the 0-30 cm soil layer. SOC concentration was determined using standardized laboratory methods, and SOC stock was calculated by integrating SOC concentration, bulk density, soil depth, and coarse fragment content. Geostatistical analyses, including experimental semivariograms and Ordinary Kriging, were applied to characterize spatial variability and to generate continuous maps of SOC concentration and SOC stock for both years (2010 and 2025). Results indicated a marked decline in mean SOC concentration, from 0.99% in 2010 to 0.85% in 2025. Correspondingly, mean SOC stock decreased from 126.48 to 84.45 Mg C/hm², representing a reduction of 33.23% and a total carbon loss of approximately 4.59 Tg C from agricultural topsoil across the province. Spatial analysis revealed increased micro-scale variability and extended spatial dependence over time, reflecting increasingly heterogeneous management effects superimposed on broader landscape controls. Severe SOC stock depletion (SOC stock ratio (the ratio of SOC stock in 2025 to that in 2010) <0.50) occurred as scattered patches across the province, while moderate declines (0.50-1.00) dominated most agricultural areas, and some northern and southern zones showed comparatively greater stability or localized carbon gains. This study provides the first long-term, spatially explicit assessment of SOC stock dynamics in western Iran, demonstrating rapid carbon depletion driven by intensive cultivation under semi-arid conditions. The findings highlight the urgent need for soil conservation and carbon-preserving management practices to sustain soil health, agricultural productivity, and climate mitigation potential in dryland farming systems.

Sustaining surface water in arid mountain-valley corridors has become increasingly difficult under a warming-wetting climate and urbanization. Focusing on the Huangshui River Basin in the Xining-Haidong Corridor of China, this study used Google Earth Engine (GEE) to process Landsat 5/7/8/9 surface reflectance imagery to reconstruct open-water dynamics within the riparian corridor. A three-expert ensemble voting framework integrated spectral water indices, Dynamic Surface Water Extent rules, and a Random Forest classifier, and the extraction results were validated using 1200 manually interpreted points. Trend analysis, Random Forest attribution, and GeoDetector were then applied to assess temporal changes and the combined effects of climate, topography, and human activity. The extraction achieved an overall accuracy of 96.17% and a Kappa coefficient of 0.923. The mapped open-water area increased from 74.33 km² in 2000 to 121.67 km² in 2024, corresponding to a net gain of 47.34 km² (63.68%). The Mann-Kendall test indicated a significant upward trend (Z=6.66; P<0.001), with a Theil-Sen slope of 1.45 km²/a. Sequential Mann-Kendall analysis identified no robust year of abrupt change, although the increasing trend became significant after 2007. Attribution results showed that topography remained the dominant spatial regulator of water persistence, as low-elevation valley floors concentrate both runoff accumulation and human land use. Population density (PD) and nighttime light (NTL) signals were stronger in urbanized reaches, where high impervious-surface values and mapped water co-occurred around managed water environments, including regulated channels, impoundments, and reservoir storage. Overlay analysis of barriers and reservoirs further suggested that engineering regulation may account for part of the persistent water patches along the corridor. These findings reveal a coupled mechanism involving climate, topography, and human regulation in shaping surface water change in a water-limited plateau river corridor, and provide evidence for water-resource management and ecological restoration in the upper Yellow River Basin.

The Jinghe River, as one of the rivers flowing into the Yellow River, China, exhibits ecosystem vulnerability to agricultural activities and urbanization. However, the impact of agricultural activities and urbanization process on the physical-chemical properties and microbial community structure in its upper reaches has not been fully documented. This study selected three distinct sections along the upper Jinghe River: the Liupan Mountains Nature Reserve (upstream), agricultural area (midstream), and urbanized area (downstream). Through combined analysis of river physical-chemical parameters with high-throughput sequencing of bacterial and fungal communities, the results indicated significant increases in river water electrical conductivity (19.60%) and ammonium nitrogen content (170.74%) from the protected area to the urban section. Conversely, significant decreases were observed in total carbon (31.74%), total organic carbon (32.60%), dissolved organic carbon (27.91%), and nitrate nitrogen concentrations (53.44%). The abundance-based coverage estimation (ACE) index of bacteria was significantly higher in urban areas than in the protected area. The ACE index of fungi and the relative abundance of Proteomycota and Ascomycota were significantly higher in the protected area than in the urban area. Co-occurrence network analysis revealed higher complexity in the microbial network within the protected area and greater modularity in agricultural area, whereas the network structure in the urban area was simplified, indicating weakened network stability due to human disturbance. Furthermore, the abundance and distribution of Acidobacteriota were significantly and negatively correlated with river carbon components (P<0.01), highlighting its crucial potential role in carbon cycling. This research aims to elucidate changes in water physical-chemical characteristics, microbial community structure, diversity, and interaction networks under human influence, providing a scientific basis for water resource management and ecological conservation in arid areas.

River ecosystems experience biodiversity loss due to intensifying anthropogenic disturbance and land use change. Although macroinvertebrate community plays a critical role in sustaining biogeochemical cycling and river ecosystem stability, the extent to which anthropogenic activities and land use changes affect macroinvertebrate biodiversity and sediment ecological health remains insufficiently resolved. This study investigated the effects of anthropogenic activities and land use changes on macroinvertebrate community diversity in the Beiluo River Basin, China. An entropy-based framework was developed to evaluate sediment ecological health. A total of 31 phyla were identified, with Annelida (1.26%-71.95%) and Arthropoda (8.25%-24.17%) as the dominant groups. The entropy values indicated that macroinvertebrate community diversity across the 18 sampling sites was the highest in the middle reaches. Mineral extraction and excessive fertilizer application in the upper and middle reaches were notably associated with sediment degradation. The expansion of riparian cropland and industrial pollution reduced benthic macroinvertebrate abundance, whereas a higher proportion of riparian water bodies buffered pollutant inputs and provided habitat refuges, thereby supporting higher macroinvertebrate abundance. In addition, hydraulic engineering and wetland conservation measures increased the diversity of Mollusca and Rotifera, reflecting positive ecological responses to policy-driven restoration. Overall, the entropy-based sediment ecological quality index (SEQI) integrating sediment chemistry with deoxyribonucleic acid (DNA) metabarcoding diversity indicated that the sediment eco-quality in the Beiluo River Basin was primarily constrained by riparian land use and catchment-scale anthropogenic pressure. Partial Least Squares-Path Modeling (PLS-PM) explained 69.30% of the variance in SEQI (goodness of fit (GOF)=0.519) and revealed that land use exerted a stronger effect on SEQI than socioeconomic factors. These findings highlight the need to curb the expansion of riparian cropland and built-up land, reduce pollutant discharges, sustain the protection of water bodies and wetlands including continued water, conservation investment, and improve wastewater treatment to protect sediment quality and benthic biodiversity. Collectively, these results provide an important basis for assessing river ecological health and informing sustainable regional socioeconomic development.

Quantifying the evolution of solar photovoltaic power potential in Xinjiang Uygur Autonomous Region (hereinafter referred as to Xinjiang) is critical for energy planning under China's carbon neutrality goals. This study applied the Equidistant Cumulative Distribution Function Matching (EDCDF_m) method to correct biases in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) multi-model data, using the fifth generation of European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis-Land (ERA5-Land) as a reference. Corrected data were used to drive 11 widely validated empirical photovoltaic models to evaluate the spatiotemporal evolution of photovoltaic power potential in Xinjiang from 2000 to 2060. The results showed that average annual power potential from 2000 to 2020 was 282.29 kWh/m², with higher values in the south and lower in the north and a slight decreasing trend. In terms of future forecasting, compared with historical baseline period, photovoltaic power potential increased slightly under the Shared Socioeconomic Pathway (SSP)1-2.6 scenario, with about 79.41% of the land area showing favorable conditions of increased photovoltaic power potential and reduced coefficient of variation (CV). In contrast, under the SSP2-4.5 and SSP5-8.5 scenarios, unfavorable pattern of decreased photovoltaic power potential and increased CV expanded significantly, covering about 24.66% and 37.65% of the total area, respectively. Analysis of influencing factors showed that solar radiation was the dominant factor controlling photovoltaic power potential, while rising temperature had a negative effect. Under the SSP5-8.5 scenario, reduced solar radiation and rising temperature were the main factors driving the decline in photovoltaic power potential. At the regional level, although the central area currently had relatively low potential, it shows a decreasing CV and continuously increasing photovoltaic power potential in the future, indicating better conditions for development. Economic analysis showed that, under standard carbon price, the levelized cost of electricity (LCOE) on the SSP1-2.6 path was the lowest, yielding significant net social benefits. This study indicates that pursuing a sustainable development path is a key to mitigating the adverse impacts of climate change, providing scientific reference for medium- and long-term photovoltaic installation planning in Xinjiang.