In recent years, traditional geophones for well seismic data acquisition have progressively been replaced by distributed acoustic sensing (DAS), a novel technique. The primary attributes of DAS are its extensive well coverage and robust adaptability to challenging acquisition situations. Unlike conventional geophones, vertical seismic profile (VSP) data obtained using DAS exhibit lower signal-to-noise ratios (SNRs) and more complex noise types. These complex and energetic perturbations pose challenges for further data analysis. Contemporary methods for mitigating noise in DAS-VSP data sometimes fail to yield complete and precise information, leading to inferior denoising quality and diminished signal recovery. We propose a hierarchical division encoder-decoder network utilizing a convolutional neural network to address this issue. This network employs spatial attention techniques for systematic reconstruction and facilitates hierarchical feature extraction according to while preserving signal integrity. It achieves this by comprehensively addressing features at all scales. Additionally, we generated the required training set by combining synthetic data with real noise, as no publicly available training sets are available for DAS-VSP data. The trained denoising network processes and analyzes both synthetic and real recordings. The experimental results demonstrate the efficacy of this technique in eliminating various types of DAS-VSP noise while preserving signal amplitude integrity and ensuring continuity of signal recovery.

Rapid industrialization and urbanization have induced great transformations in rural China. As a developing country with a vast rural population, China faces the persistent challenge of uneven development, and rural areas lag behind their urban counterparts, hindering the country’s modernization process. The relationship between the rural population and land use plays a key role in reflecting these disparities. Understanding these dynamics and coordinating rural human-land interactions is essential for advancing urban-rural integration and promoting rural revitalization. However, studies on the long-term dynamics and future trajectories of rural human–land relationships at the national scale remain limited. This study addresses these gaps by examining the spatiotemporal dynamics of rural residential land (2000–2020) using landscape metrics along the rural–urban gradient, assessing rural human-land relations via per capita residential land and the Tapio decoupling model, and projecting future trajectories (2020–2050). The results indicate that: (1) Over the past two decades, rural residential land has expanded mainly through edge-expansion, accompanied by pockets of leapfrog growth. Along the rural–urban gradient, mean patch area increased, and spatial aggregation intensified while fragmentation decreased, though patch shapes became more irregular. (2) Per capita rural residential land nearly doubled during 2000–2020, with 97.18% of cities nationwide experiencing expansion, and the growth was particularly significant in northern China. (3) A total of 79.66% of cities exhibit a pronounced, strong negative decoupling in rural human-land relationships, where shrinking rural populations coincide with expanding residential land areas. (4) Projections indicate that strong negative decoupling will intensify, with 52.02% of cities experiencing it between 2020 and 2030 and 71.31% between 2030 and 2050. To address this divergence, this study identifies four major zones and nine subzones based on distinct demographic, ecological, and socioeconomic conditions, and proposes targeted zone-priority measures. These research results provide valuable insights for promoting a more balanced, efficient, and sustainable development of the relationship between the rural population and land, and also offer references for the sustainable development of rural transformation in China.

The North China Plain (NCP) is China's primary agricultural region for wheat and maize production. It faces severe ecological degradation owing to decades of intensive winter wheat-summer maize double cropping (Con. W-M). The excessive reliance of this system on irrigation and nitrogen inputs amplified environmental risks. Although alternative cropping systems (ACSs) have been proposed to balance food security and sustainability, fragmented evidence hinders their practical implementation. Here, we conducted a meta-analysis of 77 studies (1060 observations) to evaluate three ACS categories: optimized double-cropping systems through soil and cropping patterns (Opt.W-M), crop diversification (Opt.C), and adjusted cropping intensity (2-year triple cropping: H3Y2; single cropping: H1Y1). The results revealed trade-offs between productivity and sustainability. ACS reduced grain yields by 15%-39% compared with those of Con.W-M, with Opt.C, H3Y2, and H1Y1 showing moderate (-15.7%), moderate (-15.3%), and severe (-38.6%) losses, respectively. Maize-centric systems, which leverage the hydrothermal adaptability of crops, demonstrated superior potential yield. However, ACS reduced water and nitrogen inputs by 40%-60% and 25%-58%, respectively, while lowering the carbon footprint by 13%-40%. Opt.W-M, particularly intercropping, increased yields by 21%, whereas H3Y2 stabilized yields (-15.3%) with 41% less irrigation. Economic outcomes varied: Opt.C boosted gross income by 18.6% through high-value crops but raised costs, whereas H3Y2's lower inputs made it economically viable despite marginal returns. A critical precipitation threshold (475.5 mm yr-1) guided system suitability: above this level, intensified management was applied through optimized soil and cropping patterns; below this level, reduced intensity was maintained through stabilized H3Y2. This study provides the first quantitative framework for ACS adoption in the NCP. By prioritizing maize-centric rotations and region-specific policies (e.g., subsidies for drought-tolerant H3Y2), stakeholders can mitigate groundwater depletion while maintaining 85% of traditional yields. These insights offer a model for global semi-arid regions with similar trade-offs between intensification and sustainability.

Understanding how urban morphology reshapes land surface thermal environments across seasons is an important basis for climate adaptive urban planning. However, existing studies have mainly focused on summer cooling or warm climate cities, leaving a limited systematic understanding of land surface temperature (LST) regulation in cold-region cities. To address this gap, this study uses Changchun, China, as a representative severe cold city. Seasonal LST was retrieved from Landsat 8/9 OLI/TIRS imagery acquired between 2024 and 2026, and multisource urban spatial data, urban functional zone (UFZ) delineation, and an interpretable XGBoost–SHAP framework were integrated to quantify the contributions, directional associations, and nonlinear responses of multidimensional urban morphology. The results show that LST was highest in summer and lowest in winter. High temperature areas were mainly concentrated in commercial and industrial zones within the central built-up area, whereas low temperature areas were concentrated in the southeastern ecological zone. Thermal differences among UFZs were strongest in summer and weakest in winter. The XGBoost–SHAP results identified NDBSI, PB, NDVI, AH, and AHSD as the key predictors of seasonal LST variation. NDBSI showed stable positive contributions across all seasons, while PB showed a positive nonlinear association with LST. NDVI exhibited the most evident seasonal shift, especially in ecological zones, showing negative SHAP contributions in summer but positive or weakly positive contributions in winter. This study reveals the seasonal dependence and functional differentiation of LST regulation in severe cold cities, providing evidence for thermal environment planning that moves beyond a single summer cooling oriented approach.

The evaluation of resources and environmental carrying capacity (RECC) is of great significance for achieving harmony between humans and resources and the environment to realize sustainable development. However, current research has not reached a consensus on the research objects, theories, and methods for RECC evaluation. Therefore, this study defined the research object of regional RECC evaluation and designed an evaluation process for regional RECC based on the mutation progression method developed from the mutation theory. Then, the RECCs of 16 cities in Shandong Province during 2013-2022 were calculated, and their temporal and spatial evolution characteristics were analyzed. The result shows that: (1) the research object of regional RECC evaluation is essentially the concentrated reflection of the interaction between resources, the environment, the economy, and society; (2) the process of "construct a multilevel evaluation index system-determine the mutation types of the evaluation index system-standardize the lowest level indexes-evaluate the comprehensive regional RECC" could provide reference for RECC evaluation; and (3) from 2013 to 2022, the RECC in Shandong Province showed a steady increasing trend, and the RECC in eastern and central areas in Shandong Province was relatively higher. By analyzing these results, we found that the natural background conditions, the mode of production and life, and the decisions of the central government are the important factors affecting regional RECCs.

Cultivated land fragmentation (CLF) greatly hinders broad-acre farming and the effective use of resources. The last two decades have witnessed rapid urbanization and industrialization in Jilin Province, but analyses focusing on the spatiotemporal dynamics and determinants of CLF remain insufficient. This study analyzes data from 2000 to 2020, employing the entropy weight method to quantify fragmentation intensity and conducting a spatiotemporal analysis to examine its temporal evolution. To identify influencing factors, we utilized optimal parameter-based geographical detectors (OPGD), the random forest model (RF), and SHAP values. The findings reveal a sustained increase in fragmentation intensity, which has remained generally medium-to-high. Spatially, it clusters in distribution, being higher in the eastern and western parts and lower in the middle. The year of 2010 marked a turning point for the research area, with the rate of variation in cultivated land fragmentation declining after that year. A synergistic effect of natural and socioeconomic factors is evident, with elevation and slope exerting a significant positive association with CLF. These findings offer valuable guidance for advancing large-scale agricultural practices, promoting agricultural modernization, and enhancing food security.

Achieving global sustainable development hinges on improving the sustainability of rural territorial system (RTS). As a typical social-ecological system (SES) in rural areas, assessing RTS sustainability is essential for advancing rural revitalization and urban-rural integration. However, existing studies have not sufficiently adopted an integrated and process-oriented perspective to understand the dynamics and mechanisms of RTS sustainability. To fill this gap, we established a SES framework that conceptualizes RTS sustainability across four dimensions: human system, resource system, interactions, and outcomes. We then constructed a sustainability index (SI) to assess RTS sustainability in the Beijing-Tianjin-Hebei region in 2000, 2010, and 2020, and applied a random forest model with SHAP interpretation to identify key driving factors and characterize their nonlinear effects on SI. Evolutionary types were further identified using a self-organizing map. Results revealed that SI increased by 31.70 % from 2000 to 2020. Spatially, SI was consistently higher in the southeast and lower in the northwest; by 2020, high SI values were concentrated in the suburbs of cities of Beijing and Tianjin. During 2000-2020, land flows and allocation, economic development, and spatial connectivity were regarded as key socio-economic drivers of SI, while temperature and elevation were the main natural drivers shaping SI. Five evolutionary types were identified, and the sustainability mechanisms were interpreted with support from representative village cases. Promoting RTS sustainability requires localized measures, type-specific guidelines, and mutually beneficial urban-rural relationships. These findings offer valuable insights for rural revitalization in other similar regions.

Highlights What are the main findings? A nonlinear relationship between cropland fragmentation and both production and ecological functions is identified using a restricted cubic spline model. Based on these thresholds, three functional zones were demarcated, and land use patterns for 2030 were simulated under multiple scenarios using the PLUS model. What are the implications of the main findings? The identified thresholds provide a scientific basis for delineating functional zones to balance food production and ecological conservation. Differentiated regulation strategies should be adopted for the eastern, central, and western regions to support sustainable land use planning.Highlights What are the main findings? A nonlinear relationship between cropland fragmentation and both production and ecological functions is identified using a restricted cubic spline model. Based on these thresholds, three functional zones were demarcated, and land use patterns for 2030 were simulated under multiple scenarios using the PLUS model. What are the implications of the main findings? The identified thresholds provide a scientific basis for delineating functional zones to balance food production and ecological conservation. Differentiated regulation strategies should be adopted for the eastern, central, and western regions to support sustainable land use planning.Abstract Elucidating the mechanisms by which cropland fragmentation impacts production and ecological functions is critical for ensuring food security and ecological sustainability. Using Jilin Province as a case study, this research develops a cropland fragmentation evaluation framework based on landscape pattern indices. A restricted cubic spline model is employed to quantify nonlinear relationships and identify critical thresholds between fragmentation and both production and ecological functions. Furthermore, the PLUS model is utilized to simulate land-use patterns for 2030 under three scenarios: natural development, cropland protection, and ecological protection. The primary findings are as follows: (1) From 2000 to 2023, cropland fragmentation displayed pronounced spatial heterogeneity. Fragmentation was consistently high in the eastern mountainous areas and showed significant spatial clustering; the central region maintained relatively contiguous cropland, while the western region exhibited marked spatial variability. (2) Cropland fragmentation exhibits a nonlinear negative correlation with production functions, wherein the marginal negative impact attenuates beyond a threshold of 0.340. Conversely, its association with ecological functions follows a U-shaped trajectory, with a critical inflection point at 0.363 marking a directional shift in the fragmentation-ecology nexus. (3) Based on these nonlinear thresholds, the study area was delineated into production-ecology synergy zones, dysfunctional sensitive zones, and ecosystem landscape trade-off zones. Specifically, the central agricultural core is characterized by functional synergy; the ecologically fragile western zone resides near the nadir of the U-shaped curve, rendering its balance between production and ecological functions highly vulnerable to shifts in development intensity; and the eastern ecological barrier zone manifests a distinct trade-off prioritizing ecological functions. (4) Multi-scenario simulations reveal that the natural development scenario exacerbates the expansion risk of dysfunctional sensitive zones. While the cropland protection scenario enhances production capacity, it concurrently introduces risks of ecological instability. Conversely, the ecological protection scenario effectively steers sensitive zones toward ecological recovery. Consequently, we propose a differentiated spatial regulation strategy: prioritizing land consolidation in the central region, integrating ecological restoration with capacity enhancement in the west, and sustaining ecological barriers in the east, thereby fostering sustainable regional development.

The rational distribution of urban public facilities is crucial to social and environmental welfare and exerts significant impacts on residential living conditions. While existing studies have highlighted spatial and economic impacts of urban public facilities on residential areas, a comprehensive understanding of how these facilities collectively influence urban environments, especially the specific interaction mechanisms between undesirable (Not-In-My-Backyard, NIMBY) and desirable (Yes-In-My-Backyard, YIMBY) facilities, is still limited. This study develops an integrated framework for assessing the cumulative impacts of both NIMBY and YIMBY facilities on urban environments. Through a case study of Changchun City, China, this study provides in-depth insights into the heterogeneous impacts of NIMBY and YIMBY effects on the housing market and their spatial influence ranges. The net NIMBY and YIMBY effects exhibited by public facilities may vary across different contexts. Such shifts can be attributed to the different trade-offs residents make between regional economic vitality and environmental conditions. When the air quality surrounding a YIMBY facility fails to meet residents’ environmental expectations, the YIMBY facility may exhibit a NIMBY effect. Improved socioeconomic conditions within a specific radius of a NIMBY facility may reduce the NIMBY effect, thereby generating a stronger net YIMBY effect. These insights offer valuable guidance for urban planners, enabling them to improve urban layouts, amplify the positive effects of public facilities, and reduce or even prevent potential adverse impacts.

Achieving rural revitalization requires coordinated development of population, land, and industry within the rural territorial system (RTS). However, with the growing application of geospatial big data, effective approaches to evaluating RTS coordinated development remain underexplored, and limited attention has been paid to its spatial correlation structure. To address these gaps, this study evaluated the coordinated development in municipal-level RTSs in China from 2000 to 2020 through the population-land-industry lens using two complementary dimensions: the comprehensive development index (CDI) and the matching index (MI). A selforganizing map and K-means clustering were used for classification, and a modified gravity model combined with social network analysis was employed to characterize the spatial correlation network. The results showed that both CDI and MI increased during 2000-2020, with MI growing more slowly, indicating that improvements in internal matching lagged behind comprehensive development gains. Spatially, CDI was markedly higher east of the Hu Line, especially in eastern coastal regions, whereas high MI values were mainly concentrated in eastern coastal regions and the Beijing-Tianjin-Hebei region. Four coordinated development patterns were identified, with industrial development remaining a lagging element across patterns. The spatial correlation network was loosely connected, strongly hierarchical, and low in redundancy, with RTSs associated with major eastern urban agglomerations and their surrounding areas mainly functioning as receivers in the network. Promoting rural revitalization requires attention to both coordinated population-land-industry development within individual RTSs and the broader inter-RTS network context. These findings offer insights for sustainable rural transformation in China.

The background noise in seismic records severely interferes with the extraction of effective reflection events, particularly in complex exploration environments such as deserts. The non-Gaussian and nonlinear characteristics of background noise further exacerbate the difficulty of noise reduction, impacting the accuracy of subsequent processing such as inversion and migration. In recent years, deep learning has demonstrated excellent performance in the suppression of complex seismic noise, exhibiting notable advantages over traditional denoising algorithms. However, traditional deep learning networks often focus solely on feature extraction at a single scale, which proves inadequate when handling complex and variable seismic data. To address the aforementioned problem, we propose a Multi-scale Dual-path Attention Network (MSDPA-Net) aimed at enhancing denoising effectiveness by fully leveraging the multi-scale features of seismic data. MSDPA-Net employs a multi-scale strategy for preliminary feature extraction, followed by a dual-path attention module to discriminate between signal and noise. Subsequently, it utilizes a feature interaction structure for reinforcement learning, culminating in effective information fusion and reconstruction through a reconstruction module. Experiments on simulated and field seismic data demonstrate that MSDPA-Net exhibits remarkable performance in suppressing complex seismic noise compared to traditional denoising algorithms and typical deep learning networks.

To support sustainable wetland management, this study simulated fine-scale wetland patterns under four development scenarios for 2035, providing inputs for assessing ecological sustainability in the Momoge Nature Reserve, China. Specifically, future wetland patterns were modeled by an integrated multi-objective programming and patch-generating land use simulation framework. The resulting patterns were applied to calculate four Sustainable Development Goal (SDG) 15 indicators: 15.1.2, 15.3.1, 15.5.1, and 15.9. These were further integrated into a composite wetland ecological sustainability index. The results show that (1) the simulation framework has high accuracy in simulating land use in wetland protected areas. (2) The wetland area reached the highest under the wetland protection scenario, approaching the pristine wetland pattern in 1965, while under the natural increase scenario, it decreased the most by 10.5% due to urban and agricultural expansion. (3) SDGs 15.1.2, 15.5.1, and 15.9 performed the best under the wetland protection scenario and the worst under the natural increase scenario. SDG 15.3.1 improved in all scenarios but failed to meet the target for land degradation neutrality. It reached the lowest value of 5.64% under the harmonious development scenario, while the highest value of 13.86% occurred under the wetland protection scenario due to water body expansion. (4) By 2035, none of the scenarios achieved a high level of wetland ecological sustainability. The wetland protection scenario was assessed as moderate, while all others were classified as low. Localized SDG 15 indicators effectively revealed differences in sustainability outcomes across scenarios and provided insights for targeted wetland protection and management.

Visible near-infrared (VNIR) spectroscopy offers a cost-effective solution to quantify the spatiotemporal dynamics of soil organic carbon (SOC), especially in the context of rapid advances in spectra-based local modeling approaches using large-scale soil spectral libraries. And yet, direct temporal transferability of VNIR spectroscopic modeling (applying historical models to new spectral data) and its capability to monitor temporal changes in SOC remain underexplored. To address this gap, this study uses the LUCAS Soil dataset (2009 and 2015) from France to evaluate the effectiveness of localized spectral models in detecting SOC changes. Two local learning algorithms, memory-based learning (MBL) and GLOBAL-LOCAL algorithms, were adapted to integrate spectral and soil property similarities during local training set selection, while also incorporating LUCAS 2009 soil measurements (clay, silt, sand, CEC) as covariates. These adapted local learning algorithms were then compared against global partial least squares regression (PLSR). The results demonstrated that localized models substantially outperformed global PLSR, with MBL achieving the highest accuracy for croplands, grasslands, and woodlands (R2 = 0.72–0.79, RMSE = 4.73–20.92 g/kg). Incorporating soil properties during the local learning procedure reduced spectral heterogeneity, leading to improved SOC prediction accuracy. This improvement was particularly pronounced after excluding organic soils from grasslands and woodlands, as evidenced by 13.3–21.1% decreases in the RMSE. Critically, for SOC monitoring, spectrally predicted SOC successfully identified over 70% of samples experiencing significant SOC changes (>10% loss or gain), effectively capturing the spatial patterns of SOC changes. This study demonstrated the potential of localized spectral modeling as a cost-effective tool for monitoring SOC dynamics, enabling efficient and large-scale assessments critical for sustainable soil management.

Peri-urban agriculture is emerging as a strategic part of ensuring the sustainability of cities' food supplies. Periurban areas comprise a mix of rural and urban land uses, and thus face competing demands from expanding urban development and the benefits of having agriculture based near major centers of consumers. Land zoning in peri-urban areas should mitigate risks to food systems and emphasize the importance of sustainable agricultural development. However, there is a lack of detailed geographic analyses of the risks to food systems in peri-urban areas, which are complex non-linear systems whose spatial heterogeneity is poorly understood. This study considers a typical peri-urban area in Changchun City, located in the black-soil region of northeast China. An interpretable machine learning framework using random forest and SHapley Additive exPlanations methods is employed to investigate risks to the food system and the mechanisms that underlie the spatially heterogeneous contributions of various environmental factors to that risk at the land parcel level. The impact index of comprehensive quality varies from 0.14 to 1.63, and risk accumulated primarily due to intense peri-urbanization activities. The risks were predicted to vary across the study area, being greatest in the southeast, a long-term built-up area, and decreasing to the northwest. The tested environmental factors show differing contributions to risk depending on the location: the contribution could be positive or negative, often changing at certain threshold values. The radiation angle of the built-up area was the most influential factor, significantly increasing risk when it was <37.5 degrees. Proximity to transport routes also increased risk: land within 1160 m of a railway or 250 m of a road showed an increased likelihood of being at risk. Agricultural greenway networks are recommended for inclusion in urban plans to alleviate the negative impacts of peri-urbanization on food systems. These findings could aid planners in formulating new policies, innovating governance, and preventing and controlling risks to cities' food supplies.

Current research on urban ecosystem services faces three major challenges: scale gap, disciplinary barriers, and knowledge fragmentation, necessitating a systematic assessment framework to support precise governance decisions. This study proposes a cascading "Typology-multifunctionality-supply and demand" framework to establish a full-chain research paradigm, enhancing information flow and feedback mechanisms from fundamental understanding to management implementation. A practical case study in Changchun, Northeast China, applied multisource data and interdisciplinary tools to map urban green space (UGS) typologies, assess multifunctionality, and identify supply–demand mismatches. UGS ecological functions exhibit higher values at the edges and lower values in the center, contrasting with the spatial distribution of social functions. Significant synergies exist among ecological functions, while weak trade-offs are observed between ecological and social functions, with most UGS being ecology-dominant. The mismatches of supply–demand gradually transition from social to ecological aspects from the inner ring to the outer ring of the city. The multifunctional performance characteristics of different UGS types vary significantly. The "Three-Question Decision Toolkit" (Where-What-Which) enables the optimization of UGS management by tracing decision-making processes back to typological adjustments, integrating spatial prioritization, functional enhancement, and targeted type-specific interventions. The cascading framework’s bidirectional coupling mechanism (forward analysis and feedback optimization) provide theoretical and practical tools for precision governance of high-density cities.

Globally, city region food systems (CRFS) are suffering from constant disruption of supply chains by the ongoing shocks of weather, disease, and geopolitical crisis. Prioritizing the development of peri-urban agriculture (PUA) is widely perceived as a pathway to enhancing the resilience of CRFS. However, PUA is often plagued by persistent and complex land degradation threats. There has been little research on how to combat these threats. In order to bridge this knowledge gap, we selected a representative peri-urban area in northeast China and identified the mechanisms underlying spatially heterogeneous cropland degradation using geographic detector and gradient boosting decision tree statistical tools. The results indicate that the croplands under threat of contamination and fragmentation were clustered mainly at the urban periphery. Although industrialization and the construction of infrastructure were identified as factors contributing to degradation, their negative impacts may be offset by density control, as restrictions of the density of both industrial agglomeration (<0.125/km2) and road network (<3 km/km2) were found to be instrumental. Soil fertility was affected by both trickle-down and siphonic effects in the peri-urban area, exhibiting contradictory trends under different urban growth rates. Soil fertility benefits from proper agricultural intensification, but overuse of agrochemicals not only reduces the effect but inhibits the provision of other ecological functions. Unfortunately, zoning controls fail to prevent these degradation threats, especially in areas situated within 7 km of the urban periphery. This implies that land-sparing strategies in the peri-urban area are not sufficient, and only by building a symbiotic interface that includes nature and agriculture as urban elements can the urban-rural dichotomy be overturned. Our findings support the global sustainable development of PUA.

Understanding changes of soil organic carbon (SOC) in top layers of croplands and their driving factors is a vital prerequisite in decision-making for maintaining sustainable agriculture. However, high-precision estimation of SOC of croplands at regional scale is still an issue to be solved. Based on soil samples, synthetic image of bare soil and geographical data, this paper predicted SOC density of croplands using Random Forest model in the Black Soil Region of Jilin Province, China in 2005 and 2020, and analyzed its influencing factors. Results showed that random forest model that integrates bare soil composite images improve the accuracy and robustness of SOC density prediction. From 2005 to 2020, the total SOC storage in croplands decreased from 89.96 to 82.79 Tg C with an annual decrease of 0.48 Tg C yr−1. The mean value of SOC density of croplands decreased from 3.42 to 3.32 kg/m2, and high values are distributed in middle parts. Changes of SOC represented significant heterogeneity spatially. 62.14% of croplands with SOC density greater than 4.0 kg/m2 decreased significantly, and 38.60% of croplands with SOC density between 2.5 and 3.0 kg/m2 significantly increased. Climatic factors made great contributions to SOC density, however, their relative importance (RI) to SOC density decreased from 44.65% to 37.26% during the study period. Synthetic images of bare soil constituted 23.54% and 26.29% of RI in the SOC density prediction, respectively, and the contribution of each band was quite different. The RIs of topographic and vegetation factors were low but increased significantly from 2005 to 2020. This study can aid local land managers and governmental agencies in assessing carbon sequestration potential and carbon credits, thus contributing to the protection and sustainable use of black soils.

Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

As a typical ecologically fragile region in the north of China, ecosystems in western Jilin Province have been severely damaged by a combination of natural factors and human activities. Ecological restoration sites need to be identified and viable strategies need to be developed to maximize the restoration of ecosystem functions and enhance human well-being. This study used the InVEST model, K-means clustering, and spatial statistical tools to identify priority sites for ecological restoration in western Jilin Province based on the change in ecosystem service bundles and in the human activity footprint. The results showed that provisioning services continued to increase and other services decreased and then increased during the study period. The provisioning service bundles and provisioning-regulating bundles increased continuously, the regulating service bundles, cultural service bundles and the service synergy bundles decreased continuously, and the supporting service bundles first increased and then decreased. Out of 48,005 evaluation units, 10,203 were prioritized for ecological restoration, accounting for 21.25% of the total. This study provides a scientific basis for restoring regional ecosystems and improving the supply of ecosystem services.