Accurate vegetation phenology monitoring is essential for understanding ecosystem responses to climate change. Here, we systematically evaluated five NDVI datasets, Daily NDVI (1 day), GIMMS-3G+ (15 day), MOD13C2 (1 month), PKU GIMMS (15 day), and SPOT (10 days), for estimating the start (SOS), end (EOS), and length (GSL) of the growing season across China from 2000 to 2020, validated with ground observations from 99 phenological stations and 20 flux towers. The Daily NDVI product exhibited the strongest agreement with both validation sources, achieving the highest correlation (R2 > 0.37 with ground observations; R2 > 0.45 with flux tower data), the smallest bias (|PB| < 1%), and the lowest root mean square error (10–22 days). Statistical tests confirmed that only the Daily NDVI-derived phenology showed no significant difference from ground observations, while both Daily NDVI and PKU GIMMS NDVI showed no significant difference from flux tower estimates. All products captured the broad-scale spatial patterns, SOS delayed and EOS advanced with increasing latitude and altitude, resulting in a clear south-north gradient in GSL. However, long-term trends diverged substantially. While forest SOS trends were consistent across products (showing advancement), grassland SOS trends were highly variable. More notably, EOS and GSL trends showed pronounced inter-product divergence, particularly in grasslands. Overall, this study demonstrates that temporal resolution critically impacts phenological accuracy, and the gap-free Daily NDVI product captures fine-scale vegetation dynamics, yielding more reliable phenological metrics essential for quantifying ecosystem responses to climate change.

Abstract The Huaihe River Eco-Economic Belt, located in the north-south transition zone of China, is a pivotal component of the national economic system. Quantitative analysis of the evolution and driving mechanisms of ecosystem services is essential for achieving regional sustainable development. Using land-use data from 1990, 2000, 2010, and 2020, we employed the InVEST model and the equivalent factor method to simulate variations in five ecosystem services: carbon sequestration, habitat quality, water yield, food‑material supply, and potential soil erosion. Hierarchical partitioning and stepwise regression were applied to reveal the impacts of land‑use proportions on ecosystem services. The results show that (1) carbon sequestration, habitat quality, and food‑material supply declined, while potential soil erosion and water yield increased; (2) synergistic relationships were observed among the five ecosystem services, with strong synergies between habitat quality and carbon sequestration, and between water yield and food‑material supply; (3) forest, urban and rural, and dry land proportions were the most significant factors affecting carbon sequestration and habitat quality. Forest proportion explained more than 40% of the variation, while urban and rural, dry land accounted for 30% and 17%, respectively. Land‑use proportions explained about 50% of the variation in potential soil erosion and water yield. For food‑material supply, paddy field proportion explained 37% of the variation, with dry land, urban–rural land, and forest each contributing about 15%. These findings provide valuable insights for land‑use planning and management in the Huaihe Eco‑economic Belt.

Forest canopy structural complexity is a key determinant of forest productivity and exerts a strong influence on its stability. Yet, the relationship between forest canopy structural complexity and productivity and its stability under changing environmental factors remains unresolved. Based on a LiDAR approach for quantification of forest canopy structural attributes, this study chose China’s Qinling Mountains as the study area to investigate the impact of forest canopy structural complexity on NPP and its stability across different terrain aspects and environmental conditions. The results showed that three canopy parameters (LAI, MCH, and CE), environmental factors, NPP and its stability all exhibited significant differences between the northern and southern aspects, as well as across Qinling Mountains. Canopy structural complexity and productivity stability were higher on the southern aspect. The forest canopy structural attributes and environmental factors in the Qinling Mountains jointly influenced the mean productivity, explaining 53% of the variation in the mean productivity. On the humid southern aspect, the complexity of canopy structure had a significant impact on forest productivity. Conversely, on the arid northern aspect, the relationship was not significant. The response of productivity stability to canopy structure attributes and environmental changes displayed a similar pattern. Our study revealed that the effect of forest canopy structure on productivity and its stability is heavily dependent on environmental factors. This study offers a theoretical foundation for formulating forest management strategies adapted to diverse environmental conditions from a forest canopy perspective.

Alpine ecosystems have vast amount of soil organic carbon (SOC) and are highly sensitive to climate change. Soil fungi play a crucial role in SOC cycling as decomposers but their contribution to alpine SOC dynamics across high elevation gradients remain poorly understood. Here, we explored fungal communities and their relationships with SOC content across an elevational gradient from 3,994 to 5,120 m on the Tibetan Plateau. We found that SOC content decreased by 28.3% from low to high elevation caused by the trade-offs between ectomycorrhizal fungi and non-ligninolytic saprotrophs. Higher elevation soils, with colder, wetter conditions and lower fertility, were dominated by ectomycorrhizal fungi at the expense of non-ligninolytic saprotrophic fungal species through environmental filtering and nutrient competition. Furthermore, ectomycorrhizal fungi are positively correlated with peroxidative enzyme activities, suggesting enhanced SOC turnover. Our work provides new evidence of, and insights into, the potential role of specific fungal guild trade-offs in shaping SOC fate in fragile alpine ecosystems.

Soil organic carbon (SOC) is pivotal to the terrestrial carbon cycle and climate regulation, yet its spatiotemporal dynamics and future climate responses across soil layers remain insufficiently understood in mountainous ecosystems. Taking the Qinling Mountains, a typical mountainous ecological barrier in central China with a total area of approximately 38.18 & times; 104 km2, as the study area, we analyzed historical SOC changes (1980s-2010s) and projected its future dynamics under different scenarios using a validated Random Forest model (R2 = 0.81 for 0-20 cm, SOC20; 0.73 for 0-100 cm, SOC100), and further disentangled dominant drivers. Results showed historical mean SOC density increased, with higher storage in western/central high-elevation zones and lower values in southern/eastern low-elevation areas. Climate was the primary driver of SOC20 dynamics, while SOC100 was jointly regulated by climate, vegetation, and environmental factors, indicating weakened climatic control with increasing soil depth. Precipitation increases partially offset warming-induced SOC loss, leading to small changes in regional mean SOC density, but strong spatial heterogeneity resulted in substantial total SOC stock losses (SOC20: -1.41 to -6.59 Tg C; SOC100: -6.86 to -28.76 Tg C), with net losses in high-elevation zones and gains in low-elevation areas. SOC within the whole 1 m soil profile exhibited larger climate-driven changes than topsoil. These findings advance understanding of SOC dynamics in complex mountainous ecosystems and provide key scientific insights for regional carbon cycle assessments under climate change.

The daily Normalized Difference Vegetation Index (NDVI) is a critical indicator of terrestrial carbon sequestration capacity, essential for accurately quantifying vegetation carbon sink functions and their dynamic responses to climate change. We previously developed a long-term daily NDVI dataset across China, but the over smoothed polynomial fitting method constrained reconstruction accuracy in complex scenarios, hindering precise fine-scale carbon sink monitoring. To address this limitation, we improved the reconstruction framework by integrating meteorological legacy effects and the machine learning algorithm, with a focus on validating its application value for net primary productivity (NPP) estimation. The reconstructed daily gap-free NDVI (1982–2023) shows strong consistency with original valid NDVI, achieving a national average R2 of 0.9, percentage bias (PB) of − 0.09

Soil organic carbon (SOC) is a key component of the terrestrial carbon cycle and is essential for soil fertility, directly influencing climate change and human well-being. However, it remains unclear how different spectral data sources and machine learning (ML) models can jointly influence SOC prediction performance, especially across large and heterogeneous agricultural landscapes. This study systematically evaluates combinations of spectral data sources, feature-selection methods, and ML models for SOC prediction in the farmland of the Loess Plateau (LP), a region characterized by fragmented croplands and limited carbon stock data. Based on large-scale field sampling of topsoil (0–5 cm) and measurements of SOC content and reflectance spectra from 460 samples, we evaluated 52 combinations of ML models and three categories of input data, including hyperspectral data, resampled multispectral data, and multispectral data combined with environmental variables. The results show that the hyperspectral-based model achieved the highest accuracy (R2 = 0.97 in validation), but its reliance on regional-scale hyperspectral datasets restricts its practical applications. In contrast, the multispectral-environmental integration model delivered scalable performance, offering a feasible pathway for regional SOC mapping. The mapping results in this case study show that the SOC content in the farmland of the LP ranges from 0.002 to 22.26 g·kg–1, with low SOC levels predominantly distributed in the loess hilly gully region and the central parts of the sandy and agricultural irrigation region. This study establishes a systematic framework for SOC mapping in heterogeneous agricultural landscapes and offers practical support for carbon management in agroecosystems.

Abstract Biochar applied in ecological restoration shows a significant dose-dependent effect. Therefore, determining an appropriate rate for specific environments is essential in practical applications. To this end, we integrated soil profiles and biogeochemical and microbiome datasets from six major ecosystems (agricultural, grassland, forest, coastal wetlands, desert, and polar tundra), constructed a global biochar remediation threshold map, and investigated the driving mechanisms of threshold formation. Simultaneously, differentiated application frequency strategies tailored to each ecosystem are proposed. The results showed that the appropriate thresholds for different ecosystems were 5–30 t ha−1 (agricultural), 5–40 t ha−1 (grassland), 5–40 t ha−1 (forest), 10–50 t ha−1 (coastal wetlands), 10–40 t ha−1 (desert), and 20–60 t ha−1 (polar tundra). Within the threshold range, in combination with customized application frequency, biochar enhances ecological functions by increasing soil water-holding capacity (by approximately 10–14.3%), reducing greenhouse gas emissions (by approximately 16.4–31.5%), lowering soil heavy metal content, and increasing soil organic matter. Exceeding the threshold can cause sharp fluctuations in soil pH, increases in bioavailable polycyclic aromatic hydrocarbons, and decreases in microbial diversity, thereby inhibiting remediation. Inadequate application frequency also weakens the ecological restoration efficacy of biochar. The thresholds are environmentally dependent. The threshold window can be expanded or narrowed by the joint regulation of preparation parameters, soil characteristics, climate, and human management. Based on this, we propose a framework of “threshold identification–mechanism analysis–targeted intervention” and customized application paths for ecosystems (e.g., on-farm biochar + fertilizer single/strip application, low-dose staged application in grassland, forest low-dose dispersal single intervention, combination of wetland surface and spot combined with seasonal application, desert inter-root precision single application, and zoned management of medium and low doses in polar regions). The framework provides a quantitative and mechanistic basis for formulating standards for the production and application of biochar and for promoting precise remediation. Graphical Abstract

Extreme droughts threaten ecosystem functions, stability, and health. Understanding the key regulatory mechanisms of ecosystem resistance to such droughts is crucial for safeguarding ecological health and optimizing resource management. Previous studies focused on horizontal-scale biodiversity's impact on drought resistance, with little attention to vertical structural complexity. Taking the 2022 summer extreme drought in the Yangtze River Basin (YZRB) as a case, we evaluated drought resistance and underlying regulatory mechanisms across ecosystems with different vertical structural diversity. We used a set of indicators including vegetation indices, foliage height diversity (vertical structural complexity), water use efficiency (WUE), and soil nutrient indices. Results showed 76.46% of the basin was affected, with 30.36% experiencing the most severe drought. Three vegetation indices exhibited similar spatial response patterns, declining by 5.20-6.77% in 2022 (vs. 2021) in drought-affected areas. The basin's median resistance was approximately 32.18, peaking in the severely affected middle and lower reaches. Forests showed the highest resistance, with significant differences among ecosystem types. Vertical structural complexity correlated positively with resistance (p < 0.05) and strongly with WUE (p < 0.01), indicating it enhances WUE. Random forest and structural equation models further revealed vertical structural complexity improves drought resistance mainly by positively regulating WUE. Soil nutrients directly and indirectly (via vertical structure and WUE) regulate resistance. This highlights the need to incorporate vertical structural complexity alongside horizontal biodiversity in assessing ecosystem stability under climate extremes. Overall, our study advances understanding of ecosystem drought response mechanisms via vertical structural complexity, WUE, and soil nutrition, supporting regional ecological health maintenance and management.

Upholding stable terrestrial ecosystems is integral to supporting climate regulation and planetary security. Yet, while aboveground ecosystem stability is widely described, global-scale patterns in belowground ecosystem stability and how it connects to aboveground stability remain virtually unknown. Here, we assembled a global dataset including high-resolution information on annual estimates of soil respiration from 4,544 communities and associated aboveground ecosystem productivity over the past four decades (1985-2018). We found that ecosystems with greater stability in aboveground productivity had greater long-term stability in soil respiration, with a positive and significant connection between above- and belowground stability being especially strong in arid environments. Stable temperatures played a crucial role in reinforcing the stability and coupling of above- and belowground ecosystems. Our work provides new evidence of, and insights into, the local to global connections of stability of above- and belowground biological activity, and identifies a fundamental role of temperature stability in maintaining this stability under a changing climate.

Understanding the dynamics of soil organic carbon (SOC) in the topsoil, the most sensitive part of soil profile to climate change, under future climate trajectories is vital for achieving carbon neutrality in China. However, large uncertainties and controversies exist in Earth System Model (ESM) simulations. We used a data-driven model to assess the responses of SOC to future climate change and quantified the critical biomass carbon input (i.e., net primary production, NPP) to preserve the current SOC level. Our results suggest that future warming alone may reduce the national topsoil organic carbon stock by 605.3 Tg C (1.72 %) by the end of the 21st century under the representative concentration pathway 8.5 (RCP8.5). However, the projected increase in precipitation cannot offset the negative impact of warming under all climate trajectories. We estimate that 18.5 %, 38.0 %, and 46.5 % of additional NPP are required in the 2030s, 2060s, and 2090s to offset the national SOC loss under RCP8.5, respectively. Further simulations driven by the NPP projections of ESMs suggest that the increasing NPP can confine warming-induced SOC loss within a small range and even slightly increase SOC in the 2090s under RCP4.5 and 8.5. Nevertheless, SOC dynamics show large spatial discrepancy, and regions with high SOC levels, especially Northeast and Southwest of China, have a high potential of losing carbon and deserve more attention. This work extends our knowledge about the future dynamics of topsoil organic carbon in China and can be a reference for current ESMs to produce more robust regional predictions.

Soil organic carbon (SOC) is a major terrestrial carbon reservoir, crucial for the global carbon cycle and climate change. However, the impact of urbanization-induced cropland encroachment on SOC remains underexplored. This study quantified SOC loss in the top 20 cm (SOC20) and 100 cm (SOC100) soil layers in the Jiangsu-Zhejiang-Shanghai (JZH) region from 1985 to 2019 using high-resolution land cover dataset and multi-temporal SOC maps. Our results show that the cumulative cropland encroachment area in the study area reached 18 925.65 km2, approximately three times the area of Shanghai. The encroached areas of cropland in Jiangsu, Zhejiang, and Shanghai accounted for 59.72%, 31.49%, and 8.79% of the total, respectively. The cumulative SOC100 loss in the JZH region was approximately 65.31 +/- 32.45 Tg C, with the SOC20 loss contributing about 32.97%, emphasizing the importance of deep SOC pool. The cumulative SOC20 (SOC100) losses in Jiangsu, Zhejiang, and Shanghai contributed approximately 55.36% (57.74%), 35.76% (31.96%), and 8.87% (10.3%) to the total losses in the JZH region, respectively. Moreover, the annual average SOC100 loss accounted for about 8.6% to 25.59% of the terrestrial carbon sink flux (11.24 Tg C yr-1) in the JZH region, emphasizing that SOC loss due to cropland encroachment cannot be overlooked when evaluating the regional carbon sink capacity. Additionally, the positive correlation between SOC loss and regional gross domestic product highlights the trade-off between economic development model of urban expansion through cropland encroachment and the resulting substantial SOC loss. This study emphasizes the importance of assessing the impacts of urbanization on regional SOC stocks, especially with regard to deep soil, and provides scientific insights for future urban planning and land management in this region.

1. Nitrogen (N) availability, which can be represented by the natural abundance of the stable N isotope delta N-15, is crucial to understanding ecosystem-level N dynamics. Specific ecosystems are dominated by different types of mycorrhizae, which can relate to biogeochemistry and affect ecosystem functioning. However, few studies have addressed the impacts of different mycorrhizal associations on variations in foliar delta N-15 due to climatic and soil physicochemical factors; prior instances of foliar delta N-15 modeling have not included mycorrhizal types. 2. Here, we used machine learning to produce a global map of foliar delta N-15 based on climatic, edaphic, vegetation, and dominant mycorrhizal factors. 3. The predicted global average foliar delta N-15 value was 0.69 parts per thousand. Plants in tropical areas were predicted to have significantly larger foliar delta N-15 values than plants from subtropical, temperate, and boreal areas. The mean annual temperature was identified as the primary driver of spatial foliar delta N-15 patterns. These results provide isotopic evidence of greater N limitations in temperate and boreal regions than tropical or subtropical regions. Furthermore, non-mycorrhizal plant species had the highest foliar delta N-15 values, followed by plants associated with arbuscular mycorrhizae, orchid mycorrhizae, ectomycorrhiza, then ericoid mycorrhizae. 4. Synthesis. Overall, changes in foliar delta N-15 were predicted to be closely associated with the type of mycorrhizal association. This study highlights the importance of incorporating mycorrhizal data to accurately assess patterns of foliar delta N-15 on a global scale. Ultimately, our findings contribute to a greater understanding of N cycling dynamics across plant types and global ecosystems.

The intricate interplay between hydrological and biogeochemical cycles underpins the sustainability of watershed resources, making it essential to comprehend their climate responses for adaptive strategies. Although climate change significantly influences the dynamics of the water-carbon cycle, understanding hydrobiogeochemical responses to climate change remains limited. In this study, we utilized the coupled hydrobiogeochemical model (SWAT-DayCent), known for its robust simulation of hydrological and biogeochemical processes, to evaluate how climate change influences water-carbon dynamics in the Weihe River Basin (WHRB), the largest tributary of the Yellow River. We further predicted the hydro-biogeochemical consequences using climate scenarios derived from four General Circulation Models under three Representative Concentration Pathways (low, medium, and high emissions pathways), with uncertainty analysis of future predictions. The results indicate that the net primary productivity (NPP) would rise under low and medium emissions pathway scenarios with rising temperatures and precipitation. Moreover, the WHRB shows that NPP and soil organic carbon (SOC) are more prominent in the southern parts and less in the northern parts. It is noteworthy that the continued air temperature rise could trigger a decline in SOC in the late century (2070-2099) under the high emissions scenario, though slight increments in precipitation and NPP might partially counterbalance this adverse effect. In summary, this study highlights the need for adaptive management strategies, especially under high emission scenarios, where rising temperatures may diminish SOC, necessitating policies that could enhance soil carbon sequestration and mitigate adverse climate impacts.

Accurately simulating soil organic carbon (SOC) dynamics is essential for carbon-related assessments. Process-oriented SOC models employ temperature (f(T)) and soil moisture (f(W)) response functions derived from specific conditions to simulate SOC responses to climate change, yet are widely applied in regional and global-scale studies. How these functions affect regional SOC simulations remains unclear. We evaluated the impacts of ten f(T) and nine f(W) functions using the Double Layer Carbon Model (DLCM) in the Qinling Mountains from 1982 to 2018. After calibration by Particle Swarm Optimization, DLCM estimated initial SOC with high spatial consistency (R-2 > 0.9) and less than 1 % bias against machine learning based baseline maps over 85 % of the area. Different functions led to large SOC variations (up to 37 % in topsoil and 30 % in subsoil). Their combined impacts vary significantly under climate fluctuations, highlighting the need for accurate functions to improve SOC prediction in a changing climate.

The karst geological carbon sink, formed through the chemical weathering of carbonate rocks, is an important part of the global terrestrial carbon sink. It has substantial potential and plays a crucial role in the global carbon cycle and regional carbon neutrality efforts. The fifth (AR5) and sixth (AR6) assessment reports of the Intergovernmental Panel on Climate Change (IPCC) have clearly affirmed the existence of geological carbon sinks associated with the chemical weathering of carbonate rocks, stating that carbon capture and geological storage are key mitigation schemes. However, numerous studies have shown that exogenous acids are widely involved during the chemical weathering of rocks, adding complexity to the carbon sequestration process and its driving mechanisms. This increases the uncertainty in assessing the carbon sequestration potential. Therefore, a key task is to accurately estimate the geological carbon sinks generated by the chemical weathering of carbonate rocks to resolve the problem of the global carbon sink loss, balancing the carbon budget, and achieving carbon neutrality. In this review, we examine assessments of the carbonate rocks chemical weathering carbon sink influenced by exogenous acids, focusing on the principles, frameworks and methodologies of carbon sink estimation. We also highlight recent advancements, key influencing factors, and underlying driving mechanisms. Looking ahead, we highlight key challenges in enhancing the accuracy and precision of carbonate rocks chemical weathering carbon sink assessments under the influence of exogenous acids. Addressing these issues will support more informed policy decisions on pathways to global carbon neutrality.

Enhanced rock weathering (ERW) has emerged as a promising carbon dioxide removal (CDR) strategy with the potential to modulate soil carbon sequestration, yet empirical assessments of its impacts remain limited. Here, we address this knowledge gap through a global meta-analysis synthesizing 74 publications. Synthesized results from field experiments showed that crushed rock amendment increased soil organic carbon (SOC), mineral-associated organic carbon, and particulate organic carbon by an average of up to 3.8%, 6.1%, and 7.5%, respectively, with no significant impact on dissolved organic carbon and soil inorganic carbon. SOC accrual was driven by elevated soil exchangeable Ca, increased microbial biomass, and improved soil structure, with local climate regulating these responses. Machine learning simulations of global croplands revealed pronounced site dependency in ERW impacts on SOC, which was positive in low-latitude (warm and humid) regions (40° N-30° S) but negative in high-latitude (cold and dry) regions. Additionally, the effects of ERW on SOC are dose- and duration-dependent. Our simulations indicated that application amounts of 50-500 g m-2 are optimal for maximizing SOC sequestration, with positive effects diminishing and negative impacts intensifying beyond this range. This empirical synthesis confirms the efficacy of ERW-particularly when Ca-rich silicate rocks in-promoting SOC sequestration and long-term CO2 sequestration. Maximizing the CDR potential of ERW requires integrating site-specific climatic and edaphic characteristics with optimized application amounts and duration. Our findings provide insights critical for balancing the costs and benefits of rock weathering for CDR and highlight the importance of ERW as a sustainable strategy for soil carbon management and climate change mitigation.

Mountain ecosystems exhibit unique microclimate conditions and high plant diversity, resulting in heterogeneous patterns and dynamics of soil organic carbon (SOC). Climate change strongly impacts the spatial and temporal dynamics of SOC, yet long-term spatiotemporal variations of SOC stocks in mountainous soils and their responses to climate change are not well understood. In this study, we employed machine learning to comprehensively investigate the spatiotemporal distribution patterns of SOC and their drivers in the Qinling Mountains from 2006 to 2022, and further projected future SOC trajectories under different climate scenarios. Results showed that the SOC pools within the top 20 cm were 1.20 Pg C. Forest ecosystems accounted for the largest proportion (74 %), followed by cropland (18 %), grassland (7 %), and shrub ecosystems (1 %). Overall, SOC in the Qinling Mountains significantly increased from 2006 to 2022. Nevertheless, the SOC in forest ecosystems of high-altitude regions exhibited a declining trend, suggesting that SOC in high-altitude forests is more sensitive to climate change and more likely to be lost. A structural equation model revealed that climate drivers (mean annual temperature and aridity index) negatively affected SOC through both direct and indirect pathways, which indicates the risk of soil carbon losses in mountains due to warming and drought. In contrast, gross primary productivity positively impacted SOC, underscoring the decisive role of plant carbon inputs in SOC accumulation in mountain ecosystems. Comparatively, soil characteristics and topographical features had little effect on SOC. Our projections further indicated an increase in SOC under the low-emission scenario (SSP1-1.9), while SOC would decrease under medium (SSP2-4.5) and high-emission (SSP5-8.5) scenarios. This study suggests that future global warming will lead to the loss of SOC in mountainous soils. Therefore, ecosystem protection, particularly for high-altitude forests, could effectively maintain SOC sequestration capacity and mitigate the negative impacts of climate change.

Remote sensing observations have shown an increasing trend in the vegetation leaf area index (LAI) over the past three decades, with climate change and human activities identified as the primary drivers of vegetation change. However, a challenge remains in identifying and quantifying the role of different drivers. In this study, we employed the paired land use experiment (PLUE) approach, which is based on the concept of natural comparative controlled experiments, to assess the impacts of human activities, especially land management, in the Emin River Basin within the border between China and Kazakhstan. The comparable climate, alongside the significant differences in human activities between the two sides of the Emin River, makes it ideal for applying the PLUE method. We found that during 2001 to 2022, both regions experienced similar inter-annual trends. The leaf area index (LAI) increased in both regions (Chinese region: 8.3 × 10−3 yr−1m2m−2; Kazakhstan region: 5.8 × 10−4 yr−1m2m−2), with the most significant increase observed in the Chinese cropland region (2.79 × 10−2 yr−1m2m−2). Through residual trend analysis, we found that the increase in the LAI from April to May in the Kazakhstan region was mainly positively influenced by human grazing activities. Comparatively, the LAI growth from June to August in the Chinese cropland region was mainly attributed to land managements. This study emphasizes the influence of human activities, especially land management, on vegetation and reveals the key factors affecting the LAI within different periods.