Background, aim, and scope The dammed valley farmlands are valuable land resources for agriculture on the Chinese Loess Plateau (CLP). However, these farmlands often face the problems of shallow groundwater table and the resulting soil salinization. It is generally assumed that precipitation, soil moisture, evapotranspiration (ET), and groundwater recharge are coupled in a shallow aquifer system. However, the dynamic connections of surface water and groundwater are inadequately addressed in the dammed valley farmlands on the CLP, limiting our understanding of the changing patterns of the groundwater table and their potential impact on soil salinity. We hypothesize that groundwater recharge is mainly controlled by precipitation and ET in the dammed valley farmlands on the CLP. Materials and methods To test this hypothesis, a farmland in the dammed valley near the town of Ganguyi in Baota District of Yan'an City on the northern CLP, and the dynamic changes in precipitation, soil moisture, ET and groundwater table were synchronously monitored by an eddy covariance flux tower and soil-groundwater monitoring system in 2020. The dynamic relationships among precipitation, soil moisture, evapotranspiration and groundwater recharge were analyzed. Moreover, the HYDRUS-1D model was applied to simulate the soil water balance in this work. Results (1) in the dry season of 2020, soil water beyond a depth of 1 m was not recharged, whereas in the wet season, we measured significant changes in soil moisture content to a depth of 3 m after heavy precipitation; (2) groundwater recharge in the dammed valley farmland was mostly triggered by heavy precipitation occurring in August (267 mm), which resulted in the groundwater table increasing by 0.7 m in a month; and (3) precipitation, ET and the groundwater table were connected. If the cumulative ET is higher than the precipitation, the groundwater table declines, and vice versa. Discussion In 2011, a larger area of dammed valley farmlands was created in the hilly and gully regions on the CLP with the implementation of the Gully Land Consolidation (GLC) project. The results of this study showed that groundwater recharge in the dammed valley farmlands had a close relationship with the balance of precipitation and ET. If the cumulative precipitation was higher than the ET, the groundwater table would increase. For example, the cumulative precipitation in the rainy season of 2020 was much higher than the ET, the soil and groundwater were substantially recharged by a large amount of precipitation over a short time. Moreover, the groundwater was probably and mainly recharged by preferential flow induced by heavy rainfall events, e.g. a heavy rainfall (113.3 mm/d) occurred on August 4—5, 2020, and the lag time of groundwater table rise was only 42 hours after rainfall initiation. In this study, the dammed valley farmland had a groundwater table of about 4 m. Due to shallow groundwater table and alkaline loessial properties, soil salinization widely occurred in the dammed valley farmlands of the study area. If the valley farmland does not plant high-water consumption crops, the groundwater table in the farmland will rise more significantly. Thus, strengthening drainage and suppressing groundwater tables are extremely important for soil and water management in the dammed valley farmlands on the CLP. Conclusions We concluded that the balance of precipitation and ET played a crucial role in regulating the groundwater table in the dammed valley farmlands on the CLP. If the cumulative ET exceeds the cumulative precipitation amount, the groundwater table declines, and vice versa. Recommendations and perspectives To maintain the sustainability of the valley land resources on the CLP, we suggest that planting high-water consumption corn and building reasonable drainage systems should be combined to suppress the groundwater table.]]> 2026/10/30 0:00:00 JIN Zhao, HAN Hao, ZHENG Han, CAO Guofan, ZHANG Jing 19true Background, aim, and scope Water scarcity has become a key constraint on the sustainable development of dryland agriculture in the middle reaches of the Yellow River. Yulin City, a typical resource-deficient region, is characterized by low agricultural water-use efficiency and an irrational crop water-consumption structure. This study focuses on maize, soybean, rice, and wheat, and applies the water footprint framework combined with crop evapotranspiration modeling to systematically investigate crop water consumption characteristics, blue and green water demands, and the spatiotemporal evolution of water footprints from 2000 to 2020. The objective is to quantify inter-crop differences in water use and assess their impacts on regional water stress, providing a scientific basis for optimizing cropping patterns and improving agricultural water-use efficiency. Materials and methods The study employs the FAO Penman-Monteith equation and the dual crop coefficient approach to estimate crop evapotranspiration. By integrating multi-source datasets, including meteorological variables, soil properties, irrigation practices, crop yields, and planting areas, the blue and green water demands and water footprints of maize, soybean, rice, and wheat in Yulin City from 2000 to 2020 were systematically quantified. Spatiotemporal variations and differences in water-use efficiency among crops were further analyzed. Results (1) Significant differences in crop evapotranspiration were observed, with rice showing the highest value (775.60 mm) and wheat the lowest (468.77 mm), while maize and soybean exhibited intermediate values of 571.33 mm and 609.99 mm, respectively, indicating substantial variability in crop water demand. (2) Blue and green water demands per unit area exhibited an overall fluctuating increasing trend. Rice had the highest blue water demand (6111.58 m³/hm²), and blue water demand consistently exceeded green water demand across all crops, indicating a strong dependence on irrigation water. (3) Significant differences in water footprints per unit mass were identified. Maize exhibited the lowest blue water footprint per unit mass (0.85 m³/kg), while soybean showed the highest (4.19 m³/kg). Rice had the lowest green water footprint per unit mass (0.25 m³/kg), accounting for only 17.0% of that of soybean (1.47 m³/kg). From 2000 to 2020, maize showed a slight decline in blue water footprint per unit mass, soybean and wheat decreased markedly, while rice exhibited a slight increase. For green water footprint per unit mass, maize and rice showed slight increases, whereas soybean and wheat declined. Overall, maize demonstrated the highest water-use efficiency and represents a key crop for water-saving agriculture in the region. (4) Total blue and green water footprints of major crops in Yulin City showed an overall fluctuating upward trend from 2000 to 2020. Maize remained the dominant contributor, with peak blue and green water footprints reaching 1.235×10⁹ m³ and 6.06×10⁸ m³, respectively. Soybean showed an increasing trend, while rice and wheat declined, with wheat exhibiting the most pronounced reduction (from 1.18×10⁸ m³ to <3×10⁵ m³). The crop water footprint structure gradually shifted from a diversified pattern to a maize- and soybean-dominated system, reflecting an increasing adaptation of agricultural systems to water constraints. Discussion This study systematically reveals differences in evapotranspiration, blue and green water demands, and water footprints among major crops in Yulin City, highlighting significant disparities in water dependence and water-use efficiency. Rice exhibits the highest water demand and strongest irrigation dependence, whereas maize shows relatively low blue water footprints and high-water productivity, making it suitable for cultivation in water-scarce regions. Soybean and wheat exhibit improved water-use efficiency over time due to technological progress, while rice shows a declining trend. Overall, blue water demand exceeds green water demand across all crops, indicating that irrigation remains the dominant component of agricultural water use. From 2000 to 2020, total water footprints exhibited a fluctuating upward trend, accompanied by a shift toward drought-tolerant crops such as maize and soybean, reflecting adaptive adjustments of the agricultural system under increasing water constraints. However, several limitations remain, including limited spatial and temporal resolution of data, exclusion of grey water footprints and extreme climate scenarios, and uncertainties introduced by parameter simplification. Conclusions This study reveals significant inter-crop differences and temporal dynamics in blue and green water use in Yulin City. Rice shows the highest evapotranspiration and water demand, while maize exhibits the lowest blue water footprint and highest water-use efficiency. Overall, blue water demand exceeds green water demand, indicating strong irrigation dependence in agricultural production. From 2000 to 2020, total water footprints show a fluctuating increasing trend, with a gradual shift toward maize and soybean dominated cropping systems, reflecting increasing adaptation to regional water scarcity. Recommendations and perspectives Future research should focus on the coupled effects of climate change and human activities on the evolution of blue and green water resources and their feedback mechanisms with food production and ecological conservation, while enhancing the integration of multi-source observational data and numerical models, as well as multi-scale coordinated management approaches, to improve the simulation accuracy and decision-support capacity for sustainable water resource management in arid and semi-arid regions. Future research should incorporate high-resolution datasets and multi-scenario modeling to improve the robustness of water footprint assessments.]]> 2026/10/30 0:00:00 SUN Jiaqi, SUN Congjian, WANG Xiaojun, WANG Guoqing, BAI Pengfei, ZHANG Jie 18true Background, aim, and scope In recent years, the frequency and intensity of extreme precipitation events on the Chinese Loess Plateau have increased under the influence of global climate change. As a major driving force of the regional hydrological cycle, precipitation infiltration directly affects soil moisture dynamics, nutrient migration, and soil chemical properties. Soil moisture is a key factor controlling vegetation growth and ecosystem stability, whereas soil pH strongly influences nutrient availability and microbial activity. Previous studies have mainly focused on the response of soil moisture to precipitation under different land-use types, while the coupling relationship between soil moisture variation and soil acid-base responses following heavy rainfall events remains poorly understood. Therefore, this study selected typical sloping cropland and abandoned land in the Jin-Shan Gorge region of the Chinese Loess Plateau as research objects. Changes in soil water content (SWC) and pH within a 0—300 cm soil profile before and after a single heavy rainfall event were investigated to reveal the effects of land-use type on soil moisture response and its relationship with soil pH dynamics, thereby providing a theoretical basis for land-use optimization and ecological restoration in the Chinese Loess Plateau region. Materials and methods The study was conducted in Lijiashan Village (37°38′09″N, 110°46′17″E), Qikou Town, Lüliang City, Shanxi Province, China, a typical hilly and gully region of the Chinese Loess Plateau. Sloping cropland and abandoned land were selected as representative land-use types. To minimize the influence of local microtopography, three spatially replicated sampling points (n=3) were established in an S-shaped pattern within each 10 m2 plot. Soil samples were collected before and after a heavy rainfall event. At each sampling point, soil samples were collected at 10-cm intervals from the surface (0 cm) to a depth of 300 cm, with three additional surface (0 cm) samples collected in each plot. A total of 372 undisturbed soil samples were obtained from the two land-use types before and after the rainfall event. For each soil layer, SWC was determined from the mean value of three spatial replicates, whereas soil pH was measured using a composite sample prepared by thoroughly mixing the three replicate samples. Consequently, statistical analyses were performed using data from 31 soil depths for each land-use type. Soil water content (SWC) was determined using the oven-drying method, and soil pH was measured using an FE28 benchtop pH meter. SWC and pH were measured, and their differences before and after rainfall were calculated as ΔSWC and ΔpH, respectively. Cluster analysis based on the magnitude of soil moisture variation was performed to classify the soil profile into drastic-response, primary-response, and secondary-response layers. Differences in ΔpH among response layers and between land-use types were evaluated using parametric or nonparametric statistical tests according to data distribution characteristics. Pearson correlation analysis and linear regression analysis were conducted to explore the coupling relationship between ΔSWC and ΔpH. Results Heavy rainfall significantly altered soil moisture in both land-use types, although the response intensity differed. Overall, SWC was higher in abandoned land than in sloping cropland, whereas sloping cropland exhibited a stronger response to rainfall. Specifically, the average SWC in sloping cropland increased by 40.4%, while that in abandoned land increased by 20.1%. Although SWC in abandoned land remained higher than that in sloping cropland after the rainfall event, the average difference in SWC between the two land-use types narrowed from 3.24% to 2.50%. Cluster analysis revealed similar vertical response patterns in both land-use types. The 20—110 cm soil layer was identified as the primary-response layer, where the largest increase in SWC occurred, whereas soil layers below 120 cm exhibited relatively small changes and were classified as secondary-response layers. Following the rainfall event, soil pH decreased in both land-use types. Further analysis based on moisture-response layers showed that ΔpH differed significantly between the primary-response and secondary-response layers in sloping cropland (P=0.013). The mean ΔpH values were −0.188±0.666 and −0.911±0.710, respectively. In contrast, no significant difference was observed between the two response layers in abandoned land (P=0.370). Comparisons between land-use types revealed no significant difference in ΔpH within the primary-response layer (P=0.674), whereas a significant difference was detected in the secondary-response layer (P=0.019), with sloping cropland exhibiting a stronger acidification tendency. Correlation analysis showed a significant positive relationship between ΔSWC and ΔpH in sloping cropland (r=0.373, P=0.039). Linear regression analysis also yielded significant results (R²=0.201, P=0.013). In contrast, the relationship between ΔSWC and ΔpH in abandoned land was weak and statistically insignificant (r=0.218, P=0.240). Discussion Heavy rainfall increased soil moisture in both land-use types, but the response was primarily concentrated within the 20—110 cm soil layer. This suggests that the wetting front generated by the rainfall mainly affected shallow and intermediate soil layers and was unable to penetrate deeply within a short period. This pattern indicates that the rainfall event mitigated the soil moisture disparities between the two land-use types to some extent. Such a pattern is consistent with the infiltration characteristics and soil structural properties commonly observed on the Chinese Loess Plateau. Long-term vegetation recovery in abandoned land has improved soil structure and increased organic matter content, resulting in higher overall soil moisture levels. In contrast, sloping cropland experiences stronger cultivation disturbance and possesses a relatively looser soil structure, leading to greater fluctuations in soil moisture following rainfall events. A single heavy rainfall event had limited effects on deep soil water recharge. The observed decrease in soil pH indicates that heavy rainfall can induce soil acidification, likely through the downward migration and leaching of soluble base cations during infiltration. The decrease in soil pH following the rainfall event indicates a general acidification trend. The significantly stronger acidification observed in the secondary-response layer of sloping cropland suggests that deeper soil layers are more sensitive to rainfall-induced ion transport processes. This suggests substantially stronger acidification in the secondary-response layer. Furthermore, the significant coupling relationship between ΔSWC and ΔpH in sloping cropland, but not in abandoned land, indicates that land-use type regulates the interaction between water movement and soil chemical properties. Improved vegetation cover and stronger buffering capacity in abandoned land may mitigate rainfall-induced pH fluctuations, resulting in a weaker acidification response. These findings indicate that soil moisture variation plays a stronger role in regulating soil pH dynamics in sloping cropland than in abandoned land. Conclusions The results indicate that sloping cropland is more sensitive to extreme precipitation events than abandoned land, exhibiting not only greater fluctuations in soil moisture but also a higher risk of rainfall-induced acidification. (1) A single heavy rainfall event noticeably increased soil moisture in both land-use types, with sloping cropland showing a stronger response than abandoned land. (2) Soil moisture variation was mainly concentrated within the 20—110 cm layer, whereas soil below 120 cm exhibited relatively weak responses, indicating limited deep-soil recharge from a single rainfall event. (3) Both land-use types experienced varying degrees of soil acidification after rainfall. Acidification in the secondary-response layer of sloping cropland was significantly stronger than that in the primary-response layer and also significantly stronger than that in the corresponding layer of abandoned land. (4) A significant coupling relationship between ΔSWC and ΔpH was observed in sloping cropland but not in abandoned land, demonstrating that land-use type strongly influences the coordinated response of soil moisture and soil acidity to heavy rainfall events. Recommendations and perspectives Therefore, soil and water conservation measures should be strengthened in sloping cropland to improve soil structural stability and reduce nutrient loss and soil degradation caused by heavy rainfall. Future studies should incorporate continuous rainfall events, soil organic carbon, soluble ions, and microbial community dynamics to further elucidate the mechanisms through which soil moisture changes drive soil chemical evolution under extreme precipitation conditions..]]> 2026/10/30 0:00:00 GUO Hankuo, WANG Yaxin, CHAO Jinlong, LU Yamin, ZHANG Yin, ZHAO Deyi 17true Background, aim, and scope Water use efficiency (WUE) is a key indicator of carbon-water coupling in terrestrial ecosystems and reflects the capacity of vegetation to assimilate carbon per unit water consumption. Clarifying the long-term spatiotemporal dynamics and driving mechanisms of vegetation WUE is essential for understanding ecosystem responses to environmental change and supporting regional ecohydrological management. Accordingly, this study aims to characterize the spatiotemporal evolution of vegetation WUE across China from 2000 to 2024 and quantify the contributions of its major environmental drivers. Materials and methods Based on remotely sensed gross primary productivity (GPP), evapotranspiration (ET), and multi-source environmental datasets from 2000 to 2024, this study estimated vegetation WUE across China. The Theil-Sen trend analysis, Mann-Kendall significance test, and an interpretable eXtreme Gradient Boosting-Shapley Additive Explanations (XGBoost-SHAP) framework were used to identify WUE trends, vegetation-type differences, and dominant driving factors. Results The results showed that the spatial pattern of WUE in China remained generally stable from 2000 to 2024, characterized by an evident east-high and west-low gradient. WUE values in humid and semi-humid eastern China were mostly 1.5—2.7 g/kg (calculated as carbon, same below), with local values exceeding 3.0 g/kg, whereas most areas in northwestern arid regions and the Qinghai-Xizang Plateau were below 1.2 g/kg. National annual mean WUE fluctuated between 1.28 and 1.41 g/kg and showed a significant increasing trend, with a slope of 0.0015 g/(kg·a) (p<0.05). The trend analysis indicated that 46.61% of China experienced WUE increases, including 15.09% with significant increases, while 27.04% showed decreasing trends and 26.35% remained basically stable. Across vegetation types, cultivated vegetation, broadleaf forests, and mixed coniferous-broadleaf forests showed relatively high proportions of significant WUE increases, accounting for 24.63%, 22.77%, and 25.19%, respectively, whereas alpine vegetation was dominated by stable trends, with 45.24% showing no obvious change. The contribution of driving factors followed the order of kernel normalized difference vegetation index (kNDVI) > temperature (TMP) > vapor pressure deficit (VPD) > fine particulate matter (PM_2.5) > precipitation (PRE) > solar radiation (SRAD) > relative humidity (RH) > population density (POP) > carbon dioxide (CO₂) > soil moisture (SM), with kNDVI and TMP being the dominant factors. Discussion The east-west spatial contrast of WUE was closely associated with regional differences in hydrothermal conditions, vegetation productivity, and ecosystem constraints. Higher WUE in eastern humid and semi-humid regions was mainly related to favorable water availability and stronger vegetation growth, whereas lower WUE in arid and alpine regions was constrained by water scarcity, low vegetation coverage, and fragile ecological conditions. The XGBoost-SHAP results further revealed that WUE responses to environmental factors were nonlinear, indicating that vegetation growth status, thermal conditions, and atmospheric water stress jointly regulated WUE variation. Conclusions From 2000 to 2024, vegetation WUE in China generally showed a stable spatial pattern and a significant upward temporal trend, with marked regional heterogeneity and vegetation-type differences. The observed WUE dynamics were primarily controlled by vegetation greenness and temperature, while VPD, PM_2.5, precipitation, and solar radiation exerted secondary influences. Recommendations and perspectives Future ecosystem management should adopt region-specific strategies. In eastern and intensively managed vegetation regions, improving vegetation structure and management practices may further enhance WUE, whereas in western arid and alpine regions, maintaining ecosystem stability and preventing degradation should be prioritized. Further studies should focus on the mechanisms underlying WUE declines in local areas and strengthen differentiated analyses across climatic zones and vegetation types.]]> 2026/10/30 0:00:00 OUYANG Ninglei, RUI Xiaoping, ZHANG Xuepeng, TANG Heng, XIE Yiheng 16true Background, aim, and scope Soil moisture content (SMC) is a key variable regulating the terrestrial water cycle, surface energy exchange, and carbon balance. Accurate monitoring of SMC is essential for understanding soil-vegetation interactions and supporting precision agriculture and ecological restoration. However, satellite remote sensing suffers from coarse spatial resolution and cannot capture small-scale heterogeneity. Unmanned aerial vehicle (UAV)-based multispectral remote sensing provides flexible, high-resolution observations that can bridge the scale gap. This study aims to assess the effects of UAV flight altitude and soil depth on the accuracy of multispectral SMC inversion models in a typical northern grassland ecotone and to establish an efficient quantitative retrieval framework. Materials and methods The research was conducted in Qingshan Town, Baicheng City, Jilin Province. UAV multispectral imagery was collected at altitudes of 50 m, 100 m, and 150 m, with resolution of 2.31 cm, 4.61 cm and 6.92 cm using a DJI Mavic 3M carrying Green, Red, RedEdge, and NIR sensors. Soil sampling was performed at depths of 0—10 cm and 10—20 cm. A total of 36 sampling points were selected using RTK positioning, and soil moisture was determined by the oven-drying method. Vegetation indices (NDVI, NDRE, GNDVI, LCI, CIG, SR, GRVI, etc.) were calculated from spectral reflectance, and Pearson correlation analysis was used to select indices with |r| ≥ 0.5 as input variables. Random forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms were employed to construct inversion models, and their accuracy was evaluated using R² and RMSE. Results For the RF model, the validation-set R² values for the 0—10 cm soil layer at flight altitudes of 50 m, 100 m, and 150 m were 0.79, 0.80, and 0.63, respectively, with corresponding RMSE values of 0.013, 0.015, and 0.022. For the 10—20 cm layer, the corresponding R² values were 0.69, 0.65, and 0.43, and the RMSE values were 0.019, 0.016, and 0.024. RF achieved higher validation-set R² for the 0—10 cm layer than for the 10—20 cm layer at each altitude. For the 0—10 cm layer, both 50 m and 100 m provided favorable retrieval performance: 50 m yielded a lower RMSE, whereas 100 m yielded a slightly higher R². At 150 m, RF validation-set R² was lower for both soil layers than at 50 m and 100 m. XGBoost achieved a higher validation-set R² than RF for the 10—20 cm layer at 50 m (0.80 versus 0.69). Spatial predictions revealed clear heterogeneity in SMC distribution, showing a gradual increase in moisture from northwest to southeast. The shallow layer showed greater spatial variation, while the deeper layer exhibited more stability but weaker spectral sensitivity. Discussion The decline in model accuracy with increasing flight altitude can be attributed mainly to the combined effects of spatial-resolution loss and mixed pixels. At lower altitude, the UAV imagery retains more detailed information on canopy structure, bare soil patches, and shadow distribution, so vegetation indices respond more sensitively to near-surface moisture variation. As altitude increases, the ground sampling distance becomes larger and each pixel is more likely to integrate multiple surface components, weakening the spectral contrast associated with SMC. Soil depth further modifies this response. The 0—10 cm layer is closer to the soil-atmosphere interface and is more directly linked with surface reflectance, evaporation, and short-term water redistribution, so its moisture condition can be captured more effectively by multispectral imagery. In contrast, the 10—20 cm layer is controlled more strongly by soil texture, root distribution, infiltration, and subsurface storage, and its optical signal is indirect and easily masked by vegetation cover. The algorithm comparison also indicates that RF is more suitable for the small-sample and high-heterogeneity conditions of this study because bagging and random feature selection reduce variance and improve robustness. XGBoost can fit complex nonlinear relationships, but its sequential boosting mechanism is more sensitive to noise, parameter settings, and reduced image quality at higher flight altitudes. These mechanisms explain why RF showed better stability across altitudes and soil depths, whereas XGBoost only showed local advantages under some high-quality data conditions. Conclusions The 0—10 cm soil layer at 50 m altitude provided the optimal inversion results, and the RF model outperformed XGBoost in stability and overall accuracy. UAV-based multispectral data effectively bridge the gap between satellite and field-scale SMC observations, enabling rapid and fine-scale monitoring in grassland ecosystems. Recommendations and perspectives Future studies should expand sampling density and incorporate temporal data to analyze seasonal SMC variations. Integrating UAV multispectral imagery with hyperspectral or microwave data and advanced deep learning algorithms will further enhance subsurface SMC retrieval and promote applications in precision agriculture and ecological management.]]> 2026/10/30 0:00:00 WANG Xin, XU Lu, LI Shaoyang, ZHAO Dandan, LI Yaxiao, YANG Ying, LI Jianguo, MA Hongyuan 15true Background, aim, and scope Groundwater serves as a decisive factor for vegetation survival in arid regions. The plain area of the Golmud River catchment in northeastern Qaidam Basin has a fragile ecological environment, and local vegetation is highly dependent on groundwater. This study aims to quantitatively assess the degree and spatial pattern of vegetation dependence on groundwater. Combined with field surveys, remote sensing inversion and GIS spatial analysis, a multi-index evaluation system is constructed to establish a vegetation dependence on groundwater index (VI). The research results provide scientific support for the coordinated management of groundwater and vegetation as well as ecological protection in arid areas. Materials and methods This study area is located in the Golmud River catchment, which covers a total area of 33712.12 km². The watershed exhibits a characteristic mountain-basin geomorphic pattern. An evaluation system was constructed using six indicators: evapotranspiration (ET), fractional vegetation cover (FVC), soil water content (SWC), soil salt content (SSC), water table depth (WTD), and total dissolved solids (TDS). Principal component analysis was used to assign weights, and an overlay analysis based on GIS was applied to calculate and grade the VI. Results Principal component analysis results indicate that the weights of ET, FVC, SWC, SSC, WTD, and TDS content in the evaluation system are 21.68%, 26.34%, 27.36%, 9.51%, 12.95%, and 2.16%, respectively. The final evaluation results show that the area percentages of vegetation dependency levels across the plain from "very low" to "very high" are 54.34%, 8.90%, 28.22%, 8.21%, and 0.33%, respectively. Spatially, very low dependency areas are mainly distributed in the piedmont gobi gravel zone and salt marsh, while high and very high dependency areas are concentrated along the frontal edges of the alluvial fans. Analysis of surface water connectivity reveals that as the distance from the river channel increases, the proportion of very high and high dependency areas gradually decreases, while the proportion of low and very low dependency areas progressively increases. Validation based on the groundwater depth threshold indicates that 94.87% of the sampling points in high and very high dependency zones have a water table depth of less than 3 m, which is consistent with existing research findings in the study area. Discussion The findings align with established ecohydrological principles. The identification of SWC and FVC as the primary drivers is logical; SWC represents the immediate water source available to plant roots, heavily influenced by capillary rise from shallow groundwater in this arid environment, while FVC reflects the integrated health and response of the ecosystem to water availability. The concentration of high dependency vegetation in groundwater discharge areas is a classic hydrological behavior, creating ecological refugia that are particularly vulnerable to groundwater drawdown. The validity of the assessment was confirmed through two independent verification methods. First, a buffer analysis along river channels showed a clear distance decay effect, where the proportion of medium to high dependency vegetation decreased significantly with increasing distance from the river, confirming the role of surface water and groundwater exchange. Second, a groundwater depth threshold validation demonstrated that 94.87% of the survey points located in high and very high dependency zones had a water table depth of less than 3 m, which is consistent with previously established ecological thresholds for the research area. This strong agreement confirms the credibility and robustness of the proposed evaluation framework. Conclusions The dependence of vegetation on groundwater in the Golmud River catchment plain area exhibits a distinct spatial pattern: low dependence in the northern and southern zones and high dependence along the central corridor. Areas with very low dependence, covering the largest extent, are primarily located in the piedmont gobi gravel zone and the salt marsh. In contrast, zones of high and very high dependence are concentrated along the frontal edges of the alluvial fans. This distribution is closely linked to the surface water and groundwater interaction. Areas of moderate dependence show a transitional pattern, reflecting the gradient change in vegetation sensitivity to groundwater under varying hydrogeological conditions. Principal component analysis reveals that SWC and FVC are the most significant influencing factors. SWC directly indicates the capillary rise of groundwater, while FVC represents the integrated biological response. ET and WTD indirectly influence dependence by regulating soil water availability. The weights of each indicator are highly consistent with the hydrogeological and eco-environmental characteristics of the study area, distinctly reflecting the differentiated contribution of each factor to vegetation groundwater dependency. Recommendations and perspectives Groundwater dependent vegetation ecosystems exhibit significant dynamic characteristics. In the future, it is essential to integrate climate change scenarios and dynamic monitoring data into the evaluation model to enhance its predictive capability and applicability, thereby providing more reliable decision-making support for the synergistic management of groundwater and vegetation systems and ecological conservation.]]> 2026/10/30 0:00:00 MA Shaoming, HUANG Jinting, ZHANG Yuhang, WANG Jie, HUANG Tian, YANG Zhan, LÜ Qiu 14true Background, aim, and scope Exploring the ecological restoration zoning system is of great significance for guaranteeing ecological security and realizing the construction of beautiful China. This paper tackles the absence of a systematic restoration zoning framework and generalized strategies in the Golmud River catchment via a three-dimensional perspective analysis, i.e. natural land conditions, dominant ecosystem services and typical ecological problems. The entire Golmud River catchment selects as the research region. Materials and methods Land use, land cover data and elevation data, and field-measured total soil salinity data collected in 2023. Remotely sensing information such as vegetation indices, MODIS 13Q1 product data, and meteorological data gathered from 2017 to 2023. Utilizing GIS spatial analysis techniques, an ecological importance assessment model and an ecological sensitivity evaluation model merged together. Then, a zoning framework based on the “natural context, function orientation and problem identification” is constructed to evaluate the accuracy of the zoning index through correlation analysis. Results The results show that: (1) The accuracy of the zoning index validation R² is 0.62, which reflects the characteristics of spatial gradient of ecological sensitivity in the catchment effectively. The zoning results are reasonably interpretable. (2) The total area is divided into five primary restoration areas, eight secondary restoration areas and five types of tertiary restoration areas. Specifically, those five tertiary restoration areas, i.e. the priority zones, key zones, prevention zones, maintenance and enhancement zones and conservation zones account for 5.29%, 10.50%, 9.83%, 54.48% and 19.92% of the total catchment area, respectively. (3) Differentiated ecological restoration strategies are proposed for different zones: areas with relatively mild ecological degradation should focus on natural recovery measures such as consolidating existing achievements. Whereas in priority and key zones, comprehensive remediation and intensive restoration strategies need to be adopted. Discussion On the spatial perspective, the zoning index exhibits significant spatial heterogeneity. In theory, ecologically highly sensitive areas are typically found in regions with high rainfall intensity. However, this study found that the ecologically highly sensitive areas are mainly concentrated in the central and northern parts of the catchment, where annual rainfall is only 42.2 mm. This indicates that rainfall induced erosion is not the primary factor determining the sensitivity assessment of this region. Additionally, this study only considered natural factors and neglected socio-economic factors, which may lead to certain limitations. Conclusions The “natural context, function-oriented and problem identification” ecological zoning framework and corresponding ecological restoration strategy is developed. And it provides a typical case for realizing ecosystem protection and restoration in the arid zones. Recommendations and perspectives The protection and restoration of arid areas ecosystems is of irreplaceable strategic significance in ensuring sustainable development in the region. There are deficiencies in the aspects considered in this paper. Subsequently, it is recommended to integrate multi-source data from remote sensing monitoring and actual surveys. The study should comprehensively consider natural factors, population activities and socio-economic factors, etc., further improve the zoning methodology, optimize the evaluation index system, determine the main influencing factors of ecological vulnerability, so as to provide a more accurate direction for ecological restoration.]]> 2026/10/30 0:00:00 LIU Zhuoyu, HUANG Jinting, LI Ting, HUANG Tian 13true Background, aim, and scope Mineral resource exploitation has profound impacts on regional carbon cycling and ecosystem functioning. Net primary productivity (NPP), an important indicator of vegetation growth and ecosystem carbon sequestration capacity, is widely used to evaluate ecological restoration in mining areas. This study focused on Panzhihua City and its typical mining areas to reveal the spatiotemporal dynamics of vegetation NPP from 2000 to 2023, identify the spatial extent of mining disturbances, and explore the driving mechanisms of natural and anthropogenic factors on the spatial differentiation of NPP. Materials and methods Based on remote sensing images, meteorological data, and geospatial datasets from 2000 to 2023, annual NPP at a spatial resolution of 30 m was estimated using the Carnegie-Ames-Stanford Approach (CASA) model. Sen's slope estimator, the Mann-Kendall significance test, coefficient of variation analysis, and partial correlation analysis were employed to characterize NPP trends, stability, and relationships with climatic factors. A partial least squares structural equation model (PLS-SEM) was constructed to quantify the direct and indirect effects of topography, climate, human activities, and the normalized difference vegetation index (NDVI) on the spatial differentiation of NPP. Results From 2000 to 2023, NPP exhibited an overall fluctuating increasing trend. Spatially, higher NPP values were mainly distributed in the northern part of the study area, whereas lower values occurred in the south, with 800—1200 g/(m²·a) (calculated as carbon, same below) representing the dominant NPP class. The pixel-scale NPP change rate ranged from −40.33 g/(m²·a) to 31.02 g/(m²·a). NPP in mining areas showed a two-stage pattern characterized by an initial decline followed by recovery. Buffer analysis revealed a distinct spatial gradient of mining disturbance, with the strongest impacts occurring in areas adjacent to mines. Partial correlation analysis showed that precipitation exerted a stronger influence on NPP than temperature. The PLS-SEM results showed that NDVI was consistently the dominant positive driving factor for NPP, with a total effect ranging from 0.766 to 0.842, whereas topography showed an overall positive effect, while climate and human activities generally exerted negative effects. Discussion The evolution of NPP in Panzhihua reflects the dynamic ecological response of mining ecosystems from disturbance to restoration. The overall recovery of vegetation productivity was mainly associated with ecological restoration projects and vegetation rehabilitation, whereas local mining areas remained under continuous disturbance from mining activities. The spatial gradient identified by the buffer analysis effectively captured the extent of mining impacts on surrounding ecosystems. The combined effects of natural environmental conditions and human activities jointly regulated NPP dynamics, among which improvements in vegetation cover played a key role in ecosystem recovery. Conclusions Vegetation productivity in the study area generally exhibited a recovery trend, although ecological restoration remained spatially heterogeneous across mining areas. Mining activities imposed significant spatially heterogeneous impacts on vegetation productivity. NDVI was identified as the dominant driver of NPP spatial differentiation, while topography, climate, and human activities jointly influenced vegetation productivity through direct and indirect pathways. Recommendations and perspectives Long-term monitoring of vegetation productivity in mining areas and their surrounding regions should be strengthened. Differentiated ecological restoration strategies should be implemented according to mining type and disturbance intensity. Future studies should integrate high-spatial- and high-temporal-resolution remote sensing data with multi-source environmental variables to further elucidate the mechanisms by which mining disturbances affect ecosystem functioning, thereby providing scientific support for ecological restoration assessment, territorial spatial planning, and sustainable development of resource-based cities.]]> 2026/10/30 0:00:00 LIAO Pan, DAI Xiaoai 12true Background, aim, and scope Excessive carbon emissions exacerbate global warming, exert severe impacts on ecological environments, and threaten the sustainable development of human society. Consequently, accurately assessing regional sustainability has become critically important. This study focuses on Hainan Island, a tropical island in southern China, aiming to reveal the spatiotemporal evolution and sustainability characteristics of the regional carbon footprint at a fine scale. Materials and methods Utilizing multi-source datasets, including socioeconomic statistics from the Hainan Statistical Yearbook, NASA MODIS remote sensing products (NDVI and land cover), and meteorological observations (temperature, precipitation, and solar radiation), we evaluated the regional carbon footprint from 2010 to 2021. Following the carbon emission guidelines of the Intergovernmental Panel on Climate Change (IPCC), we adopted a 500 m × 500 m grid-scale method based on an improved Carnegie-Ames-Stanford Approach (CASA) model for net primary productivity (NPP) simulation. Furthermore, five ecological indicators, the carbon sustainability index, land-based carbon intensity, income carbon intensity, per-capita carbon consumption, and carbon surplus ratio, were constructed to comprehensively assess regional ecological sustainability. Results The carbon footprint of Hainan Island exhibited a distinct annular spatial pattern, characterized by high values in coastal areas and low values in the central mountainous region. During 2010—2021, the highest annual average carbon footprint was observed in Haikou City (161.93 hm²), while the lowest was recorded in Qiongzhong County (2.70 hm²), demonstrating a tens-fold disparity between coastal and inland areas. Temporally, the provincial average carbon footprint showed a significant upward trend, increasing from 29.08 hm² in 2010 to 42.31 hm² in 2021, with an average annual growth rate of approximately 3.47%. All five ecological indicators pointed to a deteriorating trend in sustainability capacity over the study period. Specifically, the carbon surplus ratio declined continuously from 94.56% to 81.03%, and the carbon sustainability index dropped from 3.56 to 1.97. Meanwhile, land-based carbon intensity rose from 2.25 t/hm² (all values throughout this paper are reported on a carbon basis) to 3.82 t/hm²; per-capita carbon consumption increased from 0.88 t/cap to 1.29 t/cap; and income carbon intensity decreased from 37.08 g/yuan to 20.33 g/yuan. Discussion Correlation analysis revealed that the carbon footprint was significantly positively correlated with carbon emissions, permanent population, gross domestic product (GDP), and leaf area index (LAI), while showing non-significant correlations with temperature, annual precipitation, and NPP. The pronounced spatial disparity—characterized by high carbon footprint in coastal areas and low values in the central region—is primarily driven by the intense aggregation of human socioeconomic activities. Coastal cities like Haikou and Danzhou experience severe carbon deficits due to dense populations, rapid urbanization, and high-energy-consuming industrial layouts, which generate massive fossil fuel emissions that far exceed the local ecological carrying capacity. In contrast, the central mountainous regions (e.g., Qiongzhong and Wuzhishan) maintain carbon surpluses owing to strict ecological protection policies and high tropical rainforest coverage. Furthermore, the temporal upward trend aligns with the macro-economic expansion of the region, indicating a fundamental spatial mismatch between massive anthropogenic carbon sources and limited natural carbon sinks. Ultimately, the proposed grid-scale method demonstrated strong operability and reliable simulation outcomes, effectively identifying these spatial hotspots heavily impacted by human activities. Conclusions Carbon deficits are highly concentrated in economically developed coastal cities such as Haikou, Danzhou, and Dongfang, whereas the central counties and cities have long sustained a carbon surplus pattern. Most coastal regions exhibit substantial interannual variability in their carbon footprint, while changes in some central areas remain relatively marginal. Across Hainan Island, overall ecological pressure continues to intensify; carbon deficits in Haikou, Danzhou and Dongfang are particularly pronounced, thereby imposing marked pressure on the regional carbon balance. Recommendations and perspectives There is an urgent need to balance the relationship between economic development and ecological protection. Policymakers should accelerate the green transformation of the industrial structure and steadily promote the construction of the National Ecological Civilization Pilot Zone. This study provides a robust methodological framework and scientific reference for realizing sustainable human-land relationships in similar regions worldwide.]]> 2026/10/30 0:00:00 GUO Pengcheng, ZENG Jingyu, ZHAO Xin, CHENG Ling, WANG Qianfeng 11true Background, aim, and scope The mountain excavation and city construction (MECC) project in Yan’an has profoundly altered the regional land surface landscape, resulting in significant land use and land cover change (LUCC). This study aimed to quantify the magnitude, direction, and intensity of these changes and to identify their stage-specific characteristics during mountain excavation and subsequent urban construction. Materials and methods This study integrated Remote Sensing (RS), Geographic Information System (GIS), and mathematical methods to interpret high-resolution (0.5 m) satellite imagery (GeoEye-1, Pleiades, and WorldView-2) from 2009, 2015, and 2022, using multiscale segmentation and supervised classification (nearest neighbor algorithm). Three indicators—amplitude, direction, and intensity of land use change—were employed to characterize LUCC dynamics before and after the MECC project. Results (1) From 2009 to 2022, natural land types (grassland, forest land, and agricultural land) decreased by a total of 11.4 km², while construction land increased by 10.8 km². The mountain excavation phase (2009—2015) was the most drastic period, characterized by substantial reduction in natural land types and rapid expansion of unused land and construction land, whereas the urban construction phase (2015—2022) showed more moderate changes with partial recovery of natural land types but continued growth in construction land. (2) During the mountain excavation phase, 82.51% of the study area underwent land-use conversion with grassland and forest land contributing 86.18% of total outflows, primarily converting to unused land (inflow contribution rate of 61.60%) and construction land (20.01%). In the urban construction phase, 74.21% of land use changed, with unused land contributing 66.92% of total outflows, mainly converting to construction land, grassland, and forest land; construction land had the highest inflow contribution rate (50.02%), primarily derived from unused land (72.84%) and grassland (23.84%). (3) The integration indicator of land use change in Yan’an New District decreased from 10.61% during the mountain excavation phase to 6.62% during the urban construction phase. The land use degree change rate was negative (−0.13) during the mountain excavation phase and positive (0.61) during the urban construction phase, with an overall change of 0.29. Discussion Compared to other MECC projects on the Loess Plateau, such as the Lanzhou New Area, the patterns of land use transfer and changes in Yan’an New District show similarities, with the main transfer directions involving the conversion of grassland and forestland to unused land and construction land. Both the MECC projects in Lanzhou and Yan’an City show a trend of ecological quality declining initially and then improving over time. This suggests that, due to policy guidance and the rational planning of urban land use, the restoration of the regional eco-environment in the later stages of the MECC project has partially offset the negative impacts of urbanization, indicating that Yan’an New District is likely to achieve the sustainable utilization of land resources. Conclusions The MECC project had a phased impact on land use in Yan’an New District, characterized by initial destruction followed by reconstruction. The project promoted the comprehensive development and utilization of regional land, and through effective urban planning and ecological protection, some negative effects of urbanization can be mitigated. Ultimately, the project would play a positive role in protecting land resources. Recommendations and perspectives Continuous monitoring and evaluation of land use changes are essential for guiding scientific decision-making and understanding the project’s impact on the landscape. It is recommended that the data obtained from this study could be integrated with advanced remote sensing techniques, GIS methodologies, and mathematical models for more in-depth analyses of regional landscape patterns in the future. Future urban development should better coordinate construction-land expansion with ecological restoration and natural-land protection to mitigate the adverse effects of urbanization.]]> 2026/10/30 0:00:00 SONG Zhouqi, JIN Zhao, HAO Mingkui, YANG Siqi 10true Background, aim, and scope Cultivated land quality evaluation is an important scientific basis for cultivated land resource protection, quality improvement, and sustainable agricultural utilization. In mountainous areas, cultivated land is commonly characterized by complex terrain, fragmented plots, and strong spatial heterogeneity, which increases the difficulty of accurate quality assessment and targeted management. This study aimed to construct a cultivated land quality evaluation framework suitable for complex mountainous regions, reveal the spatial differentiation characteristics and dominant obstacle factors of cultivated land quality, and provide support for differentiated improvement zoning. Materials and methods Taking the Taihang Mountain area (36°—41°N, 113°—116°E) of Hebei Province, which covers 31 districts and counties belonging to five prefecture-level cities including Shijiazhuang, Baoding, Xingtai, Handan and Zhangjiakou, as the study area, and based on the Pressure-State-Response model, this study integrated multi-source data, including DEM, Sentinel-2 imagery covering the winter wheat heading and harvest stages and the maize sowing and heading stages from April to August 2022, Landsat 8, soil nutrients, and soil moisture data, to construct a comprehensive cultivated land quality evaluation index system. Slope and NDRE were used to represent production pressure, soil nutrients, soil moisture, and NDVI were selected to characterize cultivated land status, and TVDI was introduced as an indirect indicator of irrigation response. The analytic hierarchy process was used to determine indicator weights. The cultivated land quality index was then calculated, and the quality grades were classified. Furthermore, the obstacle degree model was applied to identify the dominant limiting factors, and local spatial autocorrelation analysis was used to reveal the spatial clustering characteristics of cultivated land quality. On this basis, cultivated land quality improvement zones were delineated. Results The cultivated land quality in the Taihang Mountain area of Hebei Province could be classified into grades Ⅴ to Ⅹ. Grade Ⅶ and grade Ⅷ cultivated land were the dominant types, accounting for 93.15% in total, indicating that the overall cultivated land quality was at a medium-low level. Spatially, cultivated land quality showed an obvious pattern of "higher in the east and lower in the west" and decreased with increasing elevation. The obstacle degree analysis showed that the state-type obstacles dominated by soil nutrient deficiency were the main limiting factors, accounting for 90.05%, which constituted the key constraint on the improvement of regional cultivated land quality. The local spatial autocorrelation results indicated that cultivated land quality had significant spatial clustering characteristics, with high-high and low-low clusters accounting for 65.21% of the total. According to the evaluation results, obstacle factors, and spatial clustering characteristics, the study area was divided into improvement utilization zones, protection utilization zones, and rational utilization zones, accounting for 32.28%, 29.47%, and 38.25%, respectively. Discussion The spatial pattern of cultivated land quality was closely related to the geomorphic background of the Taihang Mountain area. The eastern piedmont plain had relatively better natural and production conditions, while the western mountainous and hilly areas were more strongly constrained by elevation, slope, soil erosion risk, and fragmented land distribution. Soil nutrient deficiency was identified as the core driver of the state-type obstacles. Steep slopes and thin topsoil in mountainous terrain trigger severe soil erosion and nutrient depletion, while inadequate agricultural investment under conventional farming hinders nutrient restoration via fertilization and crop rotation in piedmont plains. The spatial clustering results further suggested that cultivated land quality was not randomly distributed, but showed clear spatial continuity, which provides a practical basis for regionalized management and targeted improvement. Conclusions The PSR-based multi-source remote sensing evaluation framework can effectively reveal the cultivated land quality pattern, limiting factors, and improvement direction in complex mountainous areas. The cultivated land quality in the Taihang Mountain area of Hebei Province is generally at a medium-low level, with obvious spatial differentiation and significant clustering characteristics. Soil nutrient deficiency is the primary factor restricting cultivated land quality improvement. The zoning results provide a scientific basis for precise improvement, differentiated management, and sustainable utilization of cultivated land resources. Recommendations and perspectives In the future, the evaluation results should be further connected with national cultivated land quality standards through field sampling and long-term monitoring. More high-resolution and multi-temporal remote sensing data should be introduced to improve the monitoring of crop growth, soil degradation, water stress, and management intensity. In addition, the ecological and economic effects of different improvement measures should be verified through field experiments and localized assessments, so as to promote the application of cultivated land quality evaluation results in land management, high-standard farmland construction, and sustainable agricultural development in mountainous regions.]]> 2026/10/30 0:00:00 KANG Huitao, ZHANG Kuo, GONG Zheng, MA Jiawang, SUN Yi, ZHANG Haoran, HU Jiahui 9true Background, aim, and scope Volatile organic compounds (VOCs) are key precursors of secondary organic aerosols (SOA) and ozone (O₃), playing a crucial role in regional complex air pollution. The Qinling Mountains periphery, located at the transition zone between the Guanzhong urban agglomeration and the Qinling Mountains forest, where atmospheric VOCs are jointly influenced by biogenic and anthropogenic sources. However, the molecular characteristics and environmental impacts of VOCs under such source interactions remain unclear, significantly limiting the formulation of effective pollution control strategies. This study aims to investigate the concentration levels, molecular composition, air-mass sources, and secondary formation potentials of atmospheric VOCs in this transition region. Materials and methods Field observations were conducted from 1 to 31 August 2021 at the Qinling Mountains Station of the Chinese Academy of Sciences (34.07°N, 108.35°E; 689 m a. s. l.). A high-resolution proton-transfer-reaction time-of-flight mass spectrometer (VOCUS-LTOF-PTR-MS) was used to measure atmospheric VOCs. Air mass backward trajectories were calculated using the HYSPLIT_4 model with NOAA (National Oceanic and Atmospheric Administration) meteorological data, and cluster analysis was performed with TrajStat v1.2 to identify source regions. The molecular characteristics of VOCs were examined in terms of elemental composition, the number of carbon and oxygen atoms, double bond equivalence (DBE), and volatility. Parameterization methods were further used to evaluate the ozone formation potential (OFP), secondary organic aerosol potential (SOAP), and source reactivity (SR) of VOCs from different sources. Results A total of 987 formula-assigned VOCs were identified during the observation period, with an average total concentration of (130.53±0.03) μg/m³. In terms of elemental composition, VOCs containing carbon, hydrogen, and oxygen (CHO) contributed the largest fraction of the total VOC mass concentration, accounting for 78.1%, indicating high atmospheric oxidation. In terms of volatility, the VOC volatility class dominated the total mass concentration, accounting for 62.7%. Backward trajectory clustering classified the air masses into biogenic transport (42.7%), anthropogenic transport (15.0%), and local mixed (42.3%) sources. Biogenic VOCs showed lower oxidation, smaller molecular weight, and higher DBE values. Anthropogenic VOCs were characterized by large, oxygen-rich molecules. The local mixed source exhibited the highest oxygen content due to the combined influence of both sources. These features reflected the influences of plant-emitted terpenoids, anthropogenic carbonyl compounds, and secondary oxidation processes. In terms of environmental impacts, CHO and VOCs containing carbon and hydrogen (CH) were the dominant contributors to ozone formation potential (OFP) and secondary organic aerosol potential (SOAP) across all sources. Source reactivity analysis further showed clear source-dependent differences: the local mixed source had the highest source reactivity for O₃ (SR(O₃)), while the biogenic transport source had the highest source reactivity for SOA (SR(SOA)). Discussion The abundant formula-assigned VOC components and the dominant contribution of oxygen-containing CHO compounds in the Qinling Mountains periphery suggest that VOCs in this transition region were chemically diverse and strongly affected by oxygenated organic compounds. The three source types showed distinct molecular characteristics. Biogenic transport was characterized by relatively higher DBE values, indicating a stronger contribution from unsaturated compounds such as plant-emitted terpenoids and their oxidation products. Anthropogenic transport showed larger molecular size and stronger oxygenated characteristics, consistent with the influence of carbonyl compounds from anthropogenic activities. The local mixed source showed the strongest oxygenated signature, suggesting that transported biogenic VOCs, anthropogenic emissions, and secondary oxidation jointly enhanced the contribution of oxygen-containing VOCs. These source-specific molecular differences further led to distinct environmental impacts: biogenic transport contributed more strongly to SOA formation potential, whereas local mixed and anthropogenic-influenced VOCs were more favorable for O₃ formation potential. Conclusions Atmospheric VOCs in the Qinling Mountains periphery showed abundant molecular composition, oxygen-containing organic compounds-dominated characteristics, and clear source dependence. Biogenic transport, anthropogenic transport, and local mixed sources differed in molecular composition, oxidation degree, carbon number distribution, and DBE characteristics, leading to distinct secondary formation potentials. Biogenic transport mainly enhanced SOA formation potential, whereas local mixed and anthropogenic-influenced VOCs were more favorable for O₃ formation potential. Recommendations and perspectives VOC control strategies in forest–urban transition regions should move beyond total VOC abundance and incorporate source-specific molecular composition and secondary formation potential. For the Qinling Mountains periphery, differentiated management should distinguish VOCs transported from forested areas from those influenced by urban emissions and local mixing. Such a source-oriented framework can provide more targeted support for coordinated O₃ and SOA pollution control in regions affected by biogenic-anthropogenic interactions.]]> 2026/10/30 0:00:00 LIU Jiahua, ZHONG Haobin, ZHAN Yanan, LIU Yi, LIN Chunshui, XU Wei, HUANG Rujin 8true Background, aim, and scope To assess the long-term improvement of eco-environmental quality driven by urban five-year plans, this study comprehensively analyzed the variations in chemical compositions, deposition fluxes and pollution sources of atmospheric precipitation at monitoring sites in urban and suburban areas of Chongqing from 2000 to 2020. This work aims to characterize the spatiotemporal evolution of acid rain and nitrogen deposition in mountainous urban regions, and provide insights into the effectiveness of regional air pollution control strategies. Materials and methods Long-term observational records of wet precipitation chemistry from urban and suburban monitoring stations during 2000—2020 were adopted in this research. Key indices including pH value, electrical conductivity, water-soluble ion concentrations and ion deposition fluxes were calculated. The Positive Matrix Factorization (PMF) receptor model was applied to quantitatively apportion the sources of water-soluble ions in precipitation. Results The mean pH values were 4.85 for the urban site and 4.49 for the suburban site. The SO₄^2-/NO₃^- ratios at both sites exceeded 3, indicating the persistence of sulfuric acid-type acid rain in Chongqing. Electrical conductivity fluctuated upward from 2000 to 2010, peaked in 2010, and then declined continuously afterwards. Water-soluble ions exhibited a comparable temporal pattern: fluctuating increase during 2000—2010 followed by a sustained decrease starting in 2011, with a higher concentration in the urban area (616.0 μeq/L) than in the suburbs (491.4 μeq/L). Ionic composition was dominated by SO₄^2- (38.41%) and Ca²⁺ (22.88%), followed by NH₄⁺ (21.16%) and NO₃^- (9.77%), showing combined sulfuric-nitric acid pollution features. Deposition fluxes of SO₄^2-, Ca²⁺ and NH₄⁺ peaked in 2004—2006 and then decreased steadily. The NO₃^- deposition flux reached its maximum in 2014 at the urban site and in 2010 at the suburban site. The urban area maintained consistently higher ionic deposition fluxes than the suburbs, among which SO₄^2- had the highest flux. The average NH₄⁺-N/NO₃^--N ratios were 2.46 and 2.08 for urban and suburban sites respectively, suggesting agricultural activities constituted the primary source of atmospheric reactive nitrogen. PMF source apportionment revealed that urban precipitation ions were mainly contributed by vehicle emissions (31.7%), coal combustion (19.4%), sea salt and biomass burning (18.7%), agricultural sources (16.8%) and dust (13.5%). For the suburban site, the source contributions ranked as agricultural sources (27.3%), sea salt and biomass burning (21.9%), vehicle emissions (20.4%), coal combustion (18.7%) and dust (11.7%). Discussion The persistent sulfuric acid-type acid rain reflected the legacy influence of historical sulfur emissions, while the concurrent contribution of nitrate indicated the gradual transformation toward compound acid pollution in Chongqing. The divergent peak time of nitrate deposition between urban and suburban regions mirrors the differing temporal trajectories of mobile source emissions and agricultural nitrogen release. The spatial disparity in ion concentration and deposition flux is associated with the differentiated intensity of anthropogenic activities between urban and suburban zones. Varied source apportionment outcomes demonstrate distinct dominant pollution sources across the two regions: traffic emissions prevail in urban core areas, whereas agricultural activities dominate suburban atmospheric wet deposition. Conclusions This study uncovered the temporal evolution and spatial disparity in chemical compositions, deposition processes and pollution sources of atmospheric precipitation between urban and suburban Chongqing. It confirms the effectiveness of emission reduction measures under five-year environmental plans, while highlighting the coexistence of sulfuric acid acid rain and reactive nitrogen pollution across this region. Recommendations and perspectives Differentiated air pollution control strategies should be implemented for urban and suburban areas to strengthen the collaborative mitigation of urban mobile source pollution and suburban agricultural nitrogen emissions. Future research can combine long-term continuous observations and numerical simulations to further explore the eco-environmental effects of wet deposition variation in mountainous areas.]]> 2026/10/30 0:00:00 LU Hui, MEN Liuxin, HUANG Wei, WANG Mengyao, ZHANG Guanli, WANG Fengwen, YU Jiayan 7true Background, aim, and scope The Yellow River Basin (YRB) is a pivotal region for China's ecological conservation and high-quality development. The region has, however, been plagued by structural issues such as short industrial chains, low product value-added, and high environmental burdens. There has been a deep-seated structural inertia rooted in extensive reliance on resource inputs. Under the strict constraints of China's goals for green and low-carbon economic transition, including the dual carbon goals, dependence on this traditional path may inhibit industrial expansion as industry comes up against ecological red lines and resource carrying capacity. The result may be a vicious cycle of economic crises and environmental disasters. The digital economy, however, as a newer form of economic activity, demonstrates immense potential to reshape production and achieve leaps in efficiency through the digitalization of production processes, the intelligent upgrading of products, and the networked restructuring of industrial supply chains. This offers a crucial solution to the YRB's dependence on resources, and may drive systemic industrial transformation. Against this background, this study aims to systematically examine how the digital economy promotes industrial transformation in the YRB, with a particular focus on its effects, underlying mechanisms, and regional heterogeneity across the provinces within the basin. Materials and methods This study used provincial panel data from 2012 to 2022 and a baseline regression model to empirically examine the mechanisms and effects of the digital economy on industrial transformation in the YRB. Results (1) The coefficients for the impact of the digital economy on the rationalization and upgrading of the industrial structure in the YRB were −0.253 and 0.138, respectively, and both statistically significant at the 1% confidence level. Further robustness tests, conducted by substituting the dependent variables, revealed that the digital economy's bidirectional promotional effects on rationalization and upgrading remained significant (p<0.01) and confirmed strong robustness. (2) Bootstrap test results indicated that the mediation coefficients of entrepreneurial growth on industrial structure rationalization and upgrading were −0.269 (p<0.05) and 0.207 (p<0.01), respectively, accounting for 59.5% and 75.5% of the total effect. (3) The development of the digital economy effectively promoted rationalization of the industrial structure in the upstream region shown by a significantly reduced Theil index (coefficient: −0.261, p<0.01). However, its impact on the middle and downstream regions was not significant. In the process of upgrading, the digital economy exerts a significant positive promotional effect across the entire basin (p<0.01), and the impact coefficients exhibit a spatial gradient pattern of downstream (0.653>midstream 0.456>upstream 0.160). Discussion This paper reveals the mechanisms through which the digital economy drives industrial transformation in the YRB, empirically testing its impact and driving pathways. The findings are largely consistent with previous literature, but it is clear that the digital economy exhibits strong characteristics of factor diffusion across time and space, and there is still work to do in understanding the cross-regional spillover effects. Conclusions (1) The digital economy drives the transformation of the industrial structure in the YRB through both rationalization and upgrading. (2) Entrepreneurial growth has played a significant mediating role in the digital economy's empowerment of industrial transformation in the YRB. (3) This empowerment exhibits spatial heterogeneity, manifested as a strong rationalization correction effect on industrial transformation in upstream regions and a spatial gradient of upgrading that diminishes progressively from downstream to upstream. Recommendations and perspectives Fiscal support and policy incentives for digital infrastructure in the YRB should be increased. Building on the national strategy of East Data and West Computing, efforts should be made to promote cross-regional data-sharing, technology transfer, and industrial collaboration, while giving greater consideration to the spatial spillover effects of the digital economy across neighboring regions. This would facilitate the free flow of resources between upstream and downstream regions, strengthen the diffusion of digital benefits across regional boundaries, and support the identification of appropriate spatial boundaries for industrial collaboration, thereby achieving coordinated industrial transformation across the whole basin. Digital technologies should also be used to modernize traditional energy-intensive and high-pollution industries and accelerate the integration of the digital and real economies in the old industrial bases along the middle and upper reaches of the Yellow River.]]> 2026/10/30 0:00:00 BAI Jun, WANG Ruilin, HE Jiali 6true Background, aim, and scope The Yellow River Basin currently faces dual challenges: a fragile ecological environment and low-quality development. Traditional evaluation systems are insufficient to address the coupling conflicts between the natural and social systems in the basin. Although notable progress has been made in ecological conservation and high-quality development, a systematic understanding of the safety and equity of key elements within the natural-social systems remains lacking. Furthermore, the safety thresholds, spatiotemporal patterns, and evolutionary trends of these key elements have yet to be clarified. This study aims to fill these research gaps. Materials and methods This study focuses on the Yellow River Basin and its nine provincial-level regions. Utilizing environmental monitoring and socio-economic data from 1990 to 2024, a sustainability assessment framework was developed based on the Safe and Just Space (SJOS) theory. The GM(1,1) model was applied to forecast development trends and identify key influencing factors. Results The findings show that: (1) in the basin scale, only chemical fertilizer application in natural systems exceeds the threshold, exceeding it by approximately 211.9%. In the social system, three indicators including education development, gender equality and sustainable cities, failed to meet the standards, with achievement rates of approximately 99.5%, 99.6% and 99.0%, respectively. Both the entire basin and its nine provincial-level regions share a common feature: the social system has more indicators exceeding the thresholds, yet it achieves a higher overall compliance level. The natural-social systems show an overall improving trend. (2) A total of 50 indicators across the nine provincial-level regions exceeded the SJOS thresholds, presenting obvious regional heterogeneity. In terms of spatial distribution, the upstream regions perform better than the midstream and downstream regions. Ranked by the number of threshold-exceeding indicators, the regions are ordered as follows: Henan (12) > Shaanxi (8) > Shandong (7) > Shanxi (5) = Qinghai (5) > Gansu (4) = Ningxia (4) > Inner Mongolia (3) > Sichuan (2). It is estimated that 50% of these threshold-exceeding indicators will return to the safe range by 2030. (3) Eight core restricting factors were identified: chemical fertilizer use, freshwater utilization, land conversion, water resources and sanitation (tap water coverage rate), industrial innovation, sustainable cities, gender equality, and decent work and economic growth. Discussion Current understanding of the key constraints on sustainable development in the Yellow River Basin varies considerably due to differences in theoretical frameworks and analytical methods. This study differentiates itself by integrating safety and justice as core dimensions, and constructing a sustainability evaluation framework for the natural-social systems from the SJOS perspective. It offers a novel analytical pathway for assessing sustainable development in the region and enhances comprehension of critical elements within this complex coupled system. Conclusions It is projected that most indicators will fall within the safe range by 2030, while the social system will face significantly greater pressure for sustainable development than the natural system. Through precise governance of the eight core obstructive elements, the Yellow River Basin has great potential for sustainable development in the future. Recommendations and perspectives This study primarily relies on macro-level assessments using statistical data. Future research could be complemented by field investigations and case studies to enable more in-depth micro-level analysis of the underlying socio-economic mechanisms that drive the formation of obstacle factors.]]> 2026/10/30 0:00:00 ZHANG Xiaoxiao, QU Xiangyu, CHEN Feng 5true Background, aim, and scope Green transformation of the tourism industry is pivotal for ecological conservation and high-quality development in the Yellow River Basin (YRB), a region of major national strategic importance. However, a comprehensive understanding of the decoupling relationship between tourism economic growth and associated carbon emissions across this vast and heterogeneous basin remains limited. This study aims to address this gap by conducting a spatiotemporal analysis of tourism-related carbon emissions and their decoupling from economic growth within the basin. Specifically, it seeks to: (1) quantify decoupling states and identify their spatiotemporal evolution from 2009 to 2023; (2) diagnose the underlying drivers of regional heterogeneity in decoupling performance; and (3) propose differentiated, context-specific policy recommendations for low-carbon tourism development across the upper, middle, and lower reaches of the YRB. Materials and methods Using panel data from nine provinces (or autonomous regions) in the YRB (2009—2023), this study applies a bottom-up carbon accounting approach and the Tapio decoupling model to assess tourism-related carbon emissions and their decoupling from tourism revenue growth. Results (1) The upper reaches exhibit characteristics of a primary development stage: low total emissions, high volatility, and weak decoupling, highlighting the tension between economic stability and ecological constraints. (2) The middle reaches are in a structural transition dilemma, with moderate emissions, steady growth, and predominantly expansive coupling, indicating persistent reliance on traditional development models. (3) The lower reaches have entered a quality-oriented phase, featuring high total emissions, low growth rates, and a shift from weak to strong decoupling, reflecting rising marginal costs of absolute emission reductions. Discussion Theoretically, the identified “mosaic” pattern featuring high volatility upstream, moderate growth midstream, and high-volume, slow growth downstream, challenges the smooth trajectory implied by the Environmental Kuznets Curve (EKC). It reveals the limitations of EKC theory in explaining transition challenges in complex and ecologically fragile regions. Practically, differentiated decoupling responses to external shocks are primarily driven by policy system resilience. Downstream provinces demonstrated rapid recovery by embedding green criteria into full-cycle governance (e.g., green consumption incentives), while upstream volatility exposed structural vulnerabilities. Thus, the key to low-carbon tourism lies not only in policy design but in building a resilient green governance system. Conclusions (1) A tripartite spatiotemporal pattern: Emissions and decoupling states vary distinctly across the upper, middle, and lower reaches, aligning with geographical and socio-economic gradients. (2) Decoupling as a diagnostic indicator: Inter-provincial variations reflect differences in tourism development models, energy structures, technological capacity, and policy resilience. (3) Policy resilience is critical: The divergent responses to external shocks underscore the importance of embedding green principles into crisis response mechanisms to sustain decoupling progress. Recommendations and perspectives To address tourism carbon emissions in the Yellow River Basin, zonal governance strategies should be implemented: the upper reaches should adopt a conservation-based low-carbon approach by strengthening ecological redline constraints and establishing ecological compensation mechanisms, focusing on low-emission formats; the middle reaches should promote structural transformation through green transition funds to advance transport electrification and energy cleaning, achieving low-carbon conversion of high-carbon assets; and the lower reaches should lead an absolute emission control path by establishing carbon budgeting and total emission control mechanisms, developing smart tourism and zero-carbon demonstration zones to provide green technology standards for the entire basin. This differentiated governance system will effectively promote the synergy between ecological protection and high-quality development in the basin.]]> 2026/10/30 0:00:00 SONG Zhufang, GUO Xiangyu, BAI Yuhu 4true Background, aim, and scope Historical and cultural space serves as an important spatial carrier of cultural heritage and reflects the long-term interaction between human activities and geographical environments. Existing studies have mainly focused on spatial distribution patterns, network structures, influencing factors, and heritage conservation; however, most are limited to single heritage types or relatively short periods, with insufficient attention to long-term spatiotemporal transitions and the pathways through which natural and human factors jointly influence historical and cultural space. The Hexi Corridor, as a key passage of the ancient Silk Road with a continuous cultural sequence from prehistoric to modern times, provides an ideal region for examining these issues. This study therefore aims to identify the long-term spatiotemporal transition characteristics of historical and cultural space in the Hexi Corridor and to clarify the associated influencing mechanisms. The findings are expected to enrich research on the long-term evolution of historical and cultural space and provide scientific support for regional cultural heritage conservation and sustainable development. Materials and methods This study takes 3121 immovable cultural heritage sites in the Hexi Corridor from the prehistoric period to the modern era as the research object, and uses methods such as kernel density, standard deviational ellipse, exploratory spatiotemporal analysis, and path analysis to systematically depict the evolution process and transition features of historical and cultural space in the Hexi Corridor, and comprehensively analyze the influence mechanism of historical and cultural space transition. Results The historical and cultural space of the Hexi Corridor generally presents a distribution characteristic of “dense in the south and sparse in the north”, with the “point-axis” structure as its core and a high dependence on oasis city nodes. Its center of gravity has undergone a “north-south-north” migration trajectory: from the southern part of Jiuquan to the southern part of Zhangye from the Prehistoric period to the Song, Liao, and Jin dynasties, it moved northward to the central part of Zhangye and stabilized during the Yuan-Ming-Qing dynasties, with no significant changes since modern times. Discussion The spatiotemporal transition of historical and cultural space in the Hexi Corridor reflects the long-term combined effects of environmental constraints and human activities. Oasis environments provide the fundamental ecological conditions for settlement continuity and cultural accumulation, while economic development enhances the capacity for cultural construction and maintenance. In contrast, the negative effect of road density suggests that intensified transportation networks may primarily reinforce existing spatial structures rather than continuously generate new centers of cultural agglomeration. Overall, the evolution of historical and cultural space is not determined by a single factor, but is jointly shaped by the integrated effects and pathway-dependent influences of natural and human factors, thereby providing a more comprehensive explanation for the long-term spatial differentiation of cultural heritage in the Hexi Corridor. Conclusions (1) The evolutionary process of the historical and cultural space in the Hexi Corridor has formed an overall pattern centered on the “point-axis” structure, featuring a north-south migration of the core focus and significant categorical differentiation. (2) The transition of historical and cultural space exhibits a spatially differentiated pattern of “stability in the north, dynamism in the south, and activity in the central region,” while its intensity shows a temporal pattern of first increasing and then decreasing. (3) Oasis area and road density exert significant effects on the spatiotemporal transition of historical and cultural space, while economic development shows a relatively strong positive effect. Overall, the spatiotemporal transition is jointly shaped by the direct and indirect effects of natural and human factors. Recommendations and perspectives Constructing a coupling analysis framework of “historical events-cultural space-environmental changes”, which combines archaeological chronology with GIS spatial analysis to accurately identify the triggering effects of key historical nodes on cultural space transition; and expanding the dimension of influencing factors by incorporating quantitative indicators such as policy intensity and ethnic integration, so as to reveal the hierarchical causal relationships of multi-scale factors and further enhance the research depth.]]> 2026/10/30 0:00:00 ZHAO Jingwen, CHEN Qiong 3true Background, aim, and scope Against the backdrop of the “Dual-Carbon” initiative, China has committed to peaking its carbon dioxide emissions before 2030 and achieving carbon neutrality by 2060. However, due to China’s vast geographical expanse, disparate resource endowments, and varying stages of regional development, the driving forces behind carbon emissions are highly complex. They exhibit strong regional heterogeneity and nonlinear dynamics that traditional linear models often fail to capture. The primary aim of this study is to systematically decode these complex, nonlinear driving mechanisms at the regional level, isolating the dominant structural drivers of provincial carbon emissions to inform targeted mitigation strategies. Materials and methods The scope of this empirical research encompasses a comprehensive analysis of 30 Chinese provincial-level administrative regions (excluding Xizang, Hong Kong, Macao and Taiwan regions), utilizing provincial panel data spanning 2008 to 2022. To fully reflect the multidimensional characteristics of regional carbon emissions, the evaluation framework constructed in this study incorporates seven explanatory variables: population size, economic development level, technological input, human capital, foreign direct investment, industrial structure and energy consumption structure. This study adopts the symbolic regression method for analysis. By iteratively generating mathematical expressions, it automatically excavates the inherent real action laws of data and captures latent nonlinear correlations and complex interaction effects among variables. Results The empirical results show that provincial carbon emissions in China exhibit profound regional heterogeneity and pronounced nonlinear features. First, regarding the spatial distribution of primary factors, the model successfully isolates each provincial-level administrative region’s dominant driver: GDP is identified as the core factor in 10 provincial-level administrative regions; population size is significant in 6 provincial-level administrative regions; FDI plays a crucial role in 5 provincial-level administrative regions; and energy consumption structure is critical in 3 provincial-level administrative regions. Furthermore, technological innovation, human capital, and industrial structure each demonstrate dominant effects in two provincial-level administrative regions. Second, based on these isolated drivers, the study categorizes the regions into four distinct typologies: economic-dominant, population-dominant, FDI-dominant, and energy-dominant types. Comparatively, GDP-dominant provincial-level administrative regions exhibit the lowest overall carbon emissions, FDI-dominant provincial-level administrative regions exhibit the highest emission levels, and energy-dominant provincial-level administrative regions display the most substantial potential for carbon mitigation. Discussion The findings of this study reveal the structural driving factors of carbon emissions across Chinese provincial-level administrative regions from multiple dimensions. Provincial-level administrative regions dominated by economic growth record the lowest total carbon emissions, and such regions have likely passed the inflection point of the Environmental Kuznets Curve. Economic expansion facilitates industrial structure optimization and generates technological spillover effects, promoting the shift toward high-value-added, low-carbon tertiary industries. On the contrary, provincial-level administrative regions dominated by foreign direct investment sustain persistently high carbon emissions, which serves as a regional manifestation of the pollution haven hypothesis. Massive foreign capital flows into energy-intensive and low-end manufacturing sectors, shifting carbon emission burdens to local regions. Energy-dominant provincial-level administrative regions boast enormous emission reduction potential. Having long relied on fossil fuels for development, these regions are trapped in fossil fuel lock-in effects, yet they constitute core areas for developing renewable energy and delivering the largest national carbon emission cuts. From a methodological perspective, the symbolic regression model presents remarkable analytical strengths. It automatically conducts rigorous variable selection for high-dimensional data and generates structurally sound, differentiated mathematical expressions tailored to each provincial-level administrative region to accommodate complex regional disparities. Moreover, it captures interactive relationships that conventional linear models fail to identify. Conclusions In conclusion, symbolic regression can help identify the carbon emission driving typologies of each provincial-level administrative region and clarify the unique interaction patterns of core factors in corresponding regions. The driving mechanisms of carbon emissions in China are marked by distinct regional features, diversified structural characteristics and prominent nonlinearity. Gross domestic product, population size, foreign direct investment and energy consumption structure are the nationwide core influencing factors, and the dominant effect of each factor shows significant geographical differentiation. Recommendations and perspectives This study thus puts forward differentiated emission reduction policy recommendations. Provincial-level administrative regions with large populations should focus on improving energy utilization efficiency and optimizing the layout of low-carbon infrastructure; economically developed provincial-level administrative regions need to continuously advance green and low-carbon transformation to achieve high-quality development. It is essential to guide foreign direct investment toward low-carbon industries. Meanwhile, all regions should simultaneously strengthen technological innovation and human capital cultivation to accelerate the implementation and popularization of low-carbon technologies. Future research can further expand the system of carbon emission influencing factors, introduce various machine learning algorithms and add model constraints, and deeply analyze the driving mechanisms of regional carbon emissions from multiple dimensions.]]> 2026/10/30 0:00:00 ZHANG Xiaoqin, LIN Xia, ZHOU Xiaoxiang 2true Background, aim, and scope Soil microorganisms are highly diverse and play a crucial role in maintaining ecosystem functions, including organic matter decomposition, nutrient cycling, and greenhouse gas regulation. Soil microbial diversity is jointly regulated by soil physicochemical properties and human activities. In rural areas, where human activities are complex and strongly superimposed, their impacts on soil microbial communities are more integrated and uncertain. Since soil microorganisms are closely linked to soil health and human well-being, understanding how rural human activities influence microbial diversity is essential for maintaining ecological balance and supporting sustainable environmental management. While current research predominantly focuses on natural or production-oriented ecosystems such as farmlands and forests, a systematic understanding integrated within the broader rural spatial context remains insufficient. Here we review how different types of rural human activities affect soil microbial diversity, indentify key limitations, particularly regarding multi-factor coupling, spatial integration, and temporal dynamics, and propose future research directions from a production-living-ecological space (PLES) perspective. Materials and methods We conducted a systematic literature review of studies published over the past decade using databases including CNKI, Web of Science, and PubMed. A total of 2456 core publications were selected using keywords such as "human activities" and "soil microbial diversity." The reviewed studies covered multiple types of human activities, including land use, farming practices, fertilization, pollution, and rural tourism. Results The results show that: (1) changes in rural soil microbial communities are driven by the combined and interactive effects of multiple human activities; (2) although increasing attention has been paid to human impacts, most studies are still limited to single-factor analyses, without adopting a multi-factor coupling perspective; (3) existing research is largely confined to production spaces (i.e., farmland systems), with limited consideration of the integrated rural spatial system and its temporal heterogeneity. Discussion With ongoing rural development, economic restructuring, and policy optimization, the patterns and intensity of rural human activities are becoming more dynamic and heterogeneous. These changes are reflected in land use transformation, diversification of farming practices, fluctuations in non-point source pollution, and shifts in settlement patterns. Such processes exert multi-dimensional and cumulative effects on soil microbial communities, making it difficult to disentangle individual drivers. Importantly, microbial responses to human activities are often nonlinear and may involve potential time-lag and threshold effects, underscoring the complexity of underlying mechanisms. Furthermore, reliance on single proxy indicator is insufficient to capture the dynamic and multidimensional nature of microbial community changes. Conclusions (1) Systematic research on the spatial effects of human activities on rural soil microorganisms remains insufficient, future studies should emphasize multi-scale spatial analysis and multi-factor interaction effects. (2) Current studies are dominated by short-term or single-scenario data, with limited long-term monitoring, thereby constraining understanding of temporal dynamics and ecosystem function stability. (3) Single indicators cannot adequately represent microbial dynamics; thus, it is necessary to construct integrated and comparable indicator systems to better quantify human activity impacts. Recommendations and perspectives Current research on rural soil microorganisms lacks a systematic and integrative analytical framework. Future research should: (1) adopt a PLES perspective to systematically interpret spatial heterogeneity and nonlinear microbial responses to human activities; (2) strengthen interdisciplinary integration by combining approaches from geography, ecology, and molecular biology to better understand microbe-environment interactions; (3) develop human activity indicator datasets and spatiotemporal transformation methods, integrating machine learning and spatial modeling to improve quantification and comparability; and (4) promote long-term monitoring approaches to better capture microbial dynamics and support rural ecosystem management. These approaches highlight the need for holistic, interdisciplinary, and data-driven research to advance rural soil microbiology and support sustainable development.]]> 2026/10/30 0:00:00 GANG Shuang, SHI Chen, FU Bo, REN Wanxia, XIAO Xiao, LI Ke, ZHANG Yingyue 1true