Due to different positions, goals, and research contents, there was no consistent geographical boundary line on Yellow River’s ‘Ji Zi Bend’. On the basis of geographical location and key ecological position within the national strategic layout, we defined Yellow River’s ‘Ji Zi Bend’ as the geographical boundary lines of “Ji-shaped” area, which started from Lanzhou City on the west till Sanmenxia City on the east, and from Baotou City on the north till the southern fringe of Sanmenxia City, forming a distribution area of the upper and middle reaches of Yellow River. The area covers 24 cities in six provinces, including Gansu Province, Ningxia Hui Autonomous Region, Inner Mongolia Autonomous Region, Shanxi Province, Shaanxi Province, and Henan Province. It includes desertified regions in northwestern area, and soil and water loss regions in southeastern area, as well as wetland and alluvial plain alongside both sides of Yellow River, with an area of about 6.04×10⁵ km² and a population of 6.01×10⁷ persons. The main ecological issues in this region are as below: scarcity and uneven distribution of water resources together with frequent occurrence of extreme climatic events; unequilibrium and poor regulation of water and sand together with serous soil and water loss; great desertification intensification and conflict between land utilization efficiency and ecological security; the conflict between energy resource exploitation and fragile ecological protection; land degradation and environmental pollution. The reasons for these aforementioned issues include: poor natural resources, and susceptible ecological conditions; history brand of desertification and soil and water loss in recent hundred years; the coupling drivers of climate change and human perturbation; the imbalance between increasing intensity of resource exploitation and utilization and regional development. More attention needs to be paid, including: enhancing management and configuration of water resources and the ability of prevention and reduction of natural disasters, being active response to climate changes; suitable measures to local real conditions and categorical policy for the improvement of ecosystem quality and the restoration and conservation of fragile ecosystems; policy coordination and management synergy, and the enhancement of holistic approach and integrating restoration. In the future, we should focus on ecological protection, concentrate on green development of energy resources, promote technological innovation in biodiversity conservation, strengthen the deep integration of biodiversity with ecological industries and green development, cultivate new quality productivity, and help win the battle against the ecological protection “bend” of the Yellow River.

Exploring the tradeoff and synergy relationship among ecosystem services in the Yellow River Delta High-Efficiency Eco-Economic Zone is of great practical significance for regional ecosystem service function zoning and high-quality development. Using the InVEST model, spatial auto-correlation and trade-off synergism (ESTD) model, we analyzed the spatial and temporal variations of five ecosystem services (habitat quality, carbon storage, soil conservation, water conservation, and water purification), as well as their trade-off and synergistic relationships at the township scale from 2000 to 2020. The results showed that habitat quality, carbon storage, and nitrogen and phosphorus output decreased as a whole from 2000 to 2020, and soil conservation and water purification increased. Habitat quality showed a distribution pattern of high in the north and low in the south, and carbon sto-rage, nitrogen and phosphorus output, soil conservation and water purification showed a pattern of low in the north and high in the south. During the study period, synergistic relationships among the five ecosystem services were predominant in both time cross-section and time period, but there were still differences, with synergistic relationships mainly between carbon storage and other services in time cross-section, and between habitat quality and other ser-vices in time period. Our results can provide theoretical guidance and practical reference for the enhancement of ecosystem services and the zoning of ecosystem functions, as well as basic support for the optimization of spatial patterns of national territory.

The Gansu section of Yellow River Basin is an important ecological barrier in China. Clarifying the trends and driving mechanisms of ecological environment change is of great significance for promoting ecological protection and sustainable high-quality development in the Yellow River Basin. Based on Google Earth Engine (GEE), we used remote sensing data from 2000 to 2022 to construct the remote sensing ecological index (RSEI) model, and analyzed the spatiotemporal changes and driving mechanisms of ecological environment quality with Theil-Sen median trend analysis, Mann-Kendall test, coefficient of variation, Hurst index, and geographic detector. The results showed that the ecological environment quality of the Gansu section of Yellow River Basin exhibited an overall periodic fluctuation with an upward trend from 2000 to 2022. Spatially, there was a pattern of higher quality in the west than in the east and higher in the south than in the north. The ecological environment quality of the study area was mainly classified into good, moderate, and poor levels. The improved areas accounted for 87.5% of the total area, and the degraded areas accounted for 12.4%. Within the study area, 76.5% of the regions exhibited rela-tively high ecological stability, while 23.6% of the regions exhibited relatively low ecological stability. It was predicted that 64.2% of the regions might face the risk of ecological degradation in the future. Precipitation was the main factor affecting the ecological environment quality, and its interaction with factors such as altitude, temperature, and land cover significantly enhanced the explanatory power for ecological environment changes. This study could provide a scientific basis for monitoring and protecting the ecological environment quality in Gansu section of Yellow River Basin.

Given their important roles in the regulation and storage functions for river flow and in the regional ecological environment and ecosystem services, natural lakes are essential for the ecological protection and high-quality development of the Yellow River Basin. We used the Landsat TM/OLI remote sensing data to analyze the area changes of Dongping Lake, Gyaring Lake, and Ngoring Lake, three representative large natural lakes in the Yellow River Basin from 1990 to 2020. We used the landscape ecology approach to study the morphological characteristics of lake shoreline and shoreland changes and the relationship between the landscape indices. The results showed that the main areas of Gyaring Lake and Ngoring Lake were mainly in the trend of expansion, while the main area of Dongping Lake significantly reduced during 1990-2000 and 2010-2020. The changes in the area of lake all occurred mainly near the lake inlet of the river. The shoreline morphology of Dongping Lake was more complex, with the fragmentation and aggregation of shoreland landscape significantly changed. The circularity ratio of Gyaring Lake gradually decreased with the expansion of the lake area, and the number of patches in its shoreland changed significantly. The fractal dimension index-mean of the shoreland of Ngoring Lake was relatively high, the complexity of its shoreline landscape was stronger, and the number of patches had increased significantly from 2000 to 2010. Meanwhile, there was a significant correlation between certain lake shoreline (shoreland) landscape indices. The changes in circularity ratio and shoreline development coefficient caused changes in the patch density of shoreland.

The Yellow River Basin is short of water resources. The dynamic monitoring of surface water area is helpful to clarify the distribution and change trend of water resources in this area. It is of great scientific significance to deeply understand the impacts of climate change and human activities on water resources and ensure the ecological security of the basin. Based on the Google Earth Engine (GEE) cloud platform, we analyzed the spatial variations of surface water area in the Yellow River Basin from 1986 to 2021 by using the mixed index algorithm, and revealed the driving factors of surface water area change in the Yellow River Basin. The results showed that the overall recognition accuracy of the water extraction algorithm based on mixing index was 97.5%. Compared with available water data products, the proposed algorithm can guarantee the integrity of the whole water area to a certain extent. The surface water area in the upper, middle, and lower reaches of the Yellow River Basin was 71.7%, 18.4%, and 9.9% of the total surface water area, respectively. From 1986 to 2021, the surface water area of the basin showed an overall upward trend, with a total increase of 3163.6 km². The surface water area of the upper, middle, and downstream regions increased by 72.0%, 22.4%, and 5.6%, respectively. The increase of precipitation was the main reason for the increase of water area, with a contribution of 55%. Vegetation restoration and construction of water conservancy projects had increased the water area of the basin. The intensification of human water extraction activity reduced the water area of the basin.

To determine the suitable planting density and row spacing of short-season cotton suitable for machine picking in the Yellow River Basin of China, we conducted a two-year field experiment in Dezhou during 2018-2019. The experiment followed a split-plot design, with planting density (82500 plants·hm^-2 and 112500 plants·hm^-2) as the main plots and row spacing (equal row spacing of 76 cm, wide-narrow row spacing of 66 cm+10 cm, equal row spacing of 60 cm) as the subplots. We examined the effects of planting density and row spacing on growth and development, canopy structure, seed cotton yield and fiber quality of short-season cotton. The results showed that plant height and LAI under high density treatment were significantly greater than those under low density treatment. The transmittance of the bottom layer was significantly lower than under low density treatment. Plant height under 76 cm equal row spacing was significantly higher than that under 60 cm equal row spacing, while that under wide-narrow row spacing (66 cm +10 cm) was significantly smaller than that under 60 cm equal row spacing in peak bolling stage. The effects of row spacing on LAI varied between the two years, densities, and growth stages. On the whole, the LAI under the wide-narrow row spacing (66 cm+10 cm) was higher, with the curve declining gently after the peak, and it was higher than that in the two cases of equal row spacing in the harvest time. The change in transmittance of the bottom layer presented the opposite trend. Density, row spacing, and their interaction had significant effects on seed cotton yield and its components. In both years, seed cotton yield was the highest (3832 kg·hm^-2 in 2018, 3235 kg·hm^-2 in 2019) under wide-narrow row spacing (66 cm+10 cm), and it was more stable at high densities. Fiber quality was less affected by density and row spacing. To sum up, the optimal density and row spacing of short-season cotton were as follows: density with 112500 plants·hm^-2 and wide-narrow row spacing (66 cm+10 cm).

The study of vegetation phenology is of great significance for understanding global climate change. The Yellow River basin has a wide spatial range and a complex ecological environment. The phenological characteristics of forest and grassland need further clarification. Based on the MODIS-EVI data from 2000 to 2018, we extracted the phenology of forest and grassland in the Yellow River basin using piecewise logistic and double logistic phenological models with the corresponding curvature change extremum method and derivative method, respectively. The temporal and spatial variations of phenological parameters were analyzed. The start of growing season (SOS) was at 90-165 day of year (DOY), and gradually delayed from southeast to northwest. The increase of 100 m elevation delayed SOS 0.94 d, and the SOS of forest was earlier than that of grassland. The end of growing season (EOS) was at 270-315 DOY, which delayed from west to southeast. For every 100 m increase in altitude, the EOS advanced 0.63 d, with EOS of forest being later than that of grassland. The length of growing season (LOS) was 110-230 d, which shortened gradually from southeast to northwest. The LOS of forest was larger than that of grassland. During the study, SOS showed an advance trend from 2000 to 2018 with a rate of 4.1 d·(10 a)^-1, and the proportion of spatial advance area was 73.2%. There was an obvious advance in the central part of the basin. EOS generally showed a significant postponement trend with a rate of 2.3 d·(10 a)^-1, and the proportion of spatially delayed area was 63.4%, the phenological advance and delay of forest was less stronger than that of grassland. LOS showed a significant prolongation trend with a rate of 6.4 d·(10 a)^-1, and the proportion of spatial extension was 71.8%. The piecewise Logistic and double Logistic phenological models and the corresponding curvature extremum method and derivative method were suitable for the extraction of natural vegetation in the Yellow River Basin. The overall LOS of forest and grassland showed a prolonging trend, which was shortened with the increases of altitude. The LOS of forest was longer than that of grassland in the study area.

The composition and stability of soil aggregates are important indicators for measuring soil quality, which would be affected by land use changes. Taking wetlands with different returning years (2 and 15 years) in the Yellow River Delta as the research object, paddy fields and natural wetlands as control, we analyzed the changes in soil physicochemical properties and soil aggregate composition. The results showed that soil water content, total organic carbon, dissolved organic carbon and total phosphorus of the returning soil (0-40 cm) showed an overall increasing trend with returning period, while soil pH and bulk density was in adverse. There was no significant change in clay content, electrical conductivity, and total nitrogen content. The contents of macro-aggregates and micro-aggregates showed overall increasing and decreasing trend with returning period, respectively. The stability of aggregates in the topsoil (0-10 cm) increased with returning years. Geometric mean diameter and mean weight diameter increased by 8.9% and 40.4% in the 15^th year of returning, respectively, while the mass proportion of >2.5 mm fraction decreased by 10.5%. There was no effect of returning on aggregates in subsoil (10-40 cm). Our results indicated that returning paddy field to wetland in the Yellow River Delta would play a positive role in improving soil structure and aggregate stability.

Exploring the evolution of production-living-ecology space and their ecological environmental effects under multiple future scenarios is of great significance for coordinating territorial spatial planning and improving environmental quality. Based on land use data from the Yellow River Delta efficient ecological economic zone in 2000, 2010, and 2020, we analyzed the distribution of production-living-ecology space and ecological environment quality. Using the PLUS model, we projected the distribution of production-living-ecology space in 2030, 2040, and 2050 under the scenarios of natural development (NDS), ecological protection (EPS), and urban development (UDS), and analyzed the ecological environment quality index and ecological contribution. The results showed that from 2000 to 2020, the area of ecological space in the study area decreased by 1413.3 km², while production and living spaces increased by 277.5 and 1136.4 km², respectively. The overall ecological environment qua-lity index declined from 0.3218 to 0.3041, before rising slightly to 0.3060, remaining at a moderate level. Model projections suggested that from 2030 to 2050, the ecological environment quality under all three scenarios would show a slow upward trend, with the EPS scenario having the highest ecological environment quality index (average value of 0.3092) and the UDS scenario the lowest (average value of 0.3082). Areas with moderate ecological environment quality were the most widespread, while high-quality areas were concentrated in mountainous regions, and low-quality areas were concentrated in urban living spaces. The transition from agricultural production land to water bodies contributed most to the improvement of ecological environment quality, while the conversion of agricultural production land to urban living land was the primary cause for the decline of quality.

Shanxi Province acts as a critical ecological barrier in the Yellow River Basin (YRB) and is an important area for ecological protection and high-quality development. With the data of land use, elevation, and gross domestic product (GDP) of the Shanxi section of the Yellow River Basin (SYRB) in 2000, 2010, and 2020, we employed the InVEST, Fragstats, and multi-scale geographically weighted regression (MGWR) models to investigate the distribution characteristics of habitat quality and its influencing factors within the SYRB. The results showed that cultivated land, forest, and grassland were the predominant land use types, followed by construction land and water area, while unused land accounted for small proportion. Between 2000 and 2020, the area of cultivated land in the SYRB had decreased and that of construction land had continuously increased. Habitat quality in the SYRB exhibited a declining trend, with a significant increase in the area of low-quality habitat, while change in high-qua-lity habitat area was not pronounced. Spatially, the habitat quality exhibited a pattern with higher values in the southeast and northwest, and lower values in the central and southwestern regions. The GDP and the rate of land urbanization negatively impacted habitat quality, exhibiting significant negative influences on the Zhongtiao Mountains in the south, the Taiyue Mountains in the east, and the Lyuliang Mountains in the west. Conversely, the aggregation index positively influenced habitat quality, with the range gradually expanding from the central area to the periphery and diminishing over time. The contagion index primarily exhibited negative impact on area with low habitat quality. The patch richness index mainly demonstrated a negative influence on area with high habitat quality. The Shannon evenness index predominantly affected habitat quality negatively in area with high vegetation coverage. Our results indicated the complex interaction between ecosystems and human activities. This study would provide a theoretical basis for the formulation of effective ecological protection policies and the sustainable development of ecosystems.

Estuarine wetlands exhibit significant interaction between fresh and salt water, with long-term carbon sequestration capability. We set up 60 sampling sites in the reed wetlands of the fresh-salt water interaction zone of the Yellow River Estuary, covering four different zones of the weak-intensity fresh-salt water interaction zone (WIZ), medium-intensity fresh-salt water interaction zone (MIZ), high-intensity interaction fresh-salt water zone (HIZ) and strong-intensity fresh-salt water interaction zone (SIZ). We investigated how fresh-salt water interaction affected the spatial variation of soil organic carbon (SOC) storage. The results showed that the area of reed wetland accounted for 17.8% of the total area of the fresh-salt water interaction zone the Yellow River Estuary, which mainly distributed in the WIZ and MIZ. The SOC content of reed wetland in the fresh-salt water interaction zone ranged from 1.09 to 3.65 g·kg^-1, the SOC density was between 1.85-5.84 kg·m^-2, and the SOC storage was (17.32±3.64)×10⁴ t. The SOC content and SOC density decreased with increasing fresh-salt water interaction. There were significant differences in surface SOC content between different subzones of the fresh-salt water interaction zone. The surface SOC content decreased significantly with the increases of fresh-salt water interaction intensity. SOC density was positively correlated with SOC, TN, NH₄⁺-N, and biomass, but negatively correlated with salt ions, soil bulk density, pH, and EC. SOC storage in the 0-30 cm soil layer accounted for 50.9%-64.2% of that in the 0-60 cm soil layer, while SOC storage in the 0-60 cm soil layer occupied 19.1%-37.7% of that in the 0-400 cm soil layer. The results could provide a scientific basis for accurately evaluating SOC storage of estuarine wetlands, improving carbon sink function and wetland management.

The temperature sensitivity of soil carbon mineralization (Q₁₀) is an important index to evaluate the responses of ecosystem carbon cycling to climate change. We examined the effects of three electron acceptors [SO₄^2-, NO₃^- and Fe(Ⅲ)] addition on the Q₁₀ value of anaerobic carbon mineralization of Phragmites australis community soil (0-10 cm) in the Yellow River Estuary wetland with the closed culture-gas chromatography method. The results showed that the three electron acceptors addition inhibited the production of CO₂ and CH₄ during the 48-day culture period, with a decrease of 17.3%-20.8% for CO₂ and 29.2%-36.2% for CH₄. Generally, the CO₂ production differed with the concentrations of electron acceptors, while CH₄ production differed with the type of electron acceptors. The CO₂:CH₄ ratios were significantly different with temperature, indicating an obvious temperature dependence for the anaerobic carbon mineralization pathway. The Q₁₀ values of CO₂ and CH₄ production under three electron acceptor additions ranged from 1.08 to 1.11 and from 1.19 to 1.37, respectively, showing an increasing trend compared with the control. The type and concentration of electron acceptors affected the temperature dependence of CO₂ production, while electron acceptors affected that of CH₄ production. It is suggested that the input of reducing salts would retard the mineralization loss of organic carbon in estuary freshwater wetlands under the background of climate change, but enhance the sensitivity of carbon mineralization to increasing temperature.

Accurate assessment of material and energy exchange between land and atmosphere is essential for water resources management and sustainable development of agriculture. To understand the characteristics of energy distribution and the dynamic change process of water and heat fluxes within the maize farmland ecosystem in the old course of Yellow River and their response to meteorological factors, we utilized the eddy covariance measurements and the full-element automatic weather station to continuously observe energy fluxes and conventional meteorological elements of summer maize farmland in the old course of Yellow River during 2019-2020. We analyzed the variation of energy fluxes and the effects of environmental factors, such as temperature, precipitation, and wind speed. Additionally, we calculated the energy closure rate and the proportion of energy distribution during the growth stage. The results showed that the peaks of net radiation, sensible heat flux, and latent heat flux occurred between 11:00 and 14:00, and the peak of soil heat flux occurred between 14:00 and 15:00. In terms of energy distribution, energy consumption of summer maize farmland during the whole growth period was dominated by latent heat flux and sensible heat flux. Energy was mainly consumed by sensible heat flux at sowing-emergence stage, accounting for 37.1% of net radiation, respectively. Energy in the rest of growth stages was dominated by latent heat flux. The energy closure rate during the whole growth period was better, with a coefficient of determination of 0.83, and the closure rate was higher in day and lower at night. Precipitation affected latent heat flux and sensible heat flux, and latent heat flux was more sensitive to precipitation. The increase of latent heat flux after rainfall was lower in late growth stage than in early growth stage. During the whole growth period of summer maize, solar radiation was the most significant meteorological factor affecting both sensible heat flux and latent heat flux, followed by vapor pressure deficit. The contribution of temperature and vapor pressure deficit to latent heat flux was significantly higher than sensible heat flux, while the relative contribution of wind speed, relative humidity, and solar radiation to latent heat flux was lower than sensible heat flux. Leaf area index and fractional vegetation cover had a significant positive correlation with latent heat flux and a significant negative correlation with sensible heat flux. Our results could deepen the understanding of water and heat transfer law of summer maize farmland in the old course of Yellow River, providing a theoretical basis for efficient water use of crops.

Shanxi Province holds an important strategic position in the overall ecological pattern of the Yellow River Basin. To investigate the changes of the ecological environment in the Shanxi section of the Yellow River Basin from 2000 to 2020, we selected MODIS remote sensing image data to determine the remote sensing ecological index (RSEI) based on the principal component analysis of greenness, humidity, dryness, and heat. Then, we analyzed the spatial and temporal variations of ecological quality in this region to explore the influencing factors. We further used the CA-Markov model to simulate and predict the ecological environment under different development scenarios in the Shanxi section of the Yellow River Basin in 2030. The results showed that RSEI had good applicability in the Shanxi section of the Yellow River Basin which could be used to monitor and evaluate the spatiotemporal variations in its ecological environment. From 2000 to 2020, the Shanxi section of the Yellow River Basin was dominated by low quality habitat areas, in which the ecological environment quality continued to improve from 2000 to 2010 and decreased from 2010 to 2020. The high quality habitat areas mainly located on the mountainous areas with superior natural conditions and rich biodiversity, while the low ecological quality areas were mainly in the Taiyuan Basin and the northern part of the study area, where the mining industry developed well. Climate factors were negatively correlated with ecological environment quality in the northern and central parts of the study area, and positively correlated with that in the mountainous area. Under all three development scenarios, the area of cultivated land, forest, water and construction land increased in 2030 compared to that in 2020. Compared to the natural development scenario and the cultivated land protection scenario, the ecological constraint scenario with RSEI as the limiting factor had the highest area of new forest and the lowest expansion rate of cultivated land and construction land. The results would provide a reference for land space planning and ecological environment protection in the Shanxi section of the Yellow River Basin.

To uncover the vapor source, formation mechanism, and the influence of meteorological factors on precipitation in the saline land of the Yellow River Delta, I employed stable isotopes of precipitation, especially for δ¹⁷O and ¹⁷O-excess, along with the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), to analyze the isotopic variation characteristics of precipitation and water vapor sources at different temporal scales and precipitation intensities [(<5, 5-10, 10-25, 25-50, >50 mm·d^-1)] from May to October in Dongying, located in the Yellow River Delta. There were wide ranges of isotopes in the daily precipitation data between May and October, with smaller ranges and enriched average values during the dry season. The slope between δ′¹⁸O and δ′¹⁷O was the minimum of 0.5211 when precipitation intensity was below 5 mm·d^-1, indicating the potential influence of evaporation from the moisture source site on precipitation. The maximum value was 0.5268 when precipitation intensity was between 10 mm·d^-1 and 25 mm·d^-1. For precipitation intensities below 50 mm·d^-1 with four different intensities, δ²H, δ¹⁸O and δ¹⁷O decreased with the increase of precipitation. During the dry season, ¹⁷O-excess exhibited a positive relationship with temperature, suggesting the influence of continental circulating water vapor on precipitation. Conversely, in the wet season, ¹⁷O-excess displayed a negative relationship with relative humidity (RH), indicating less influence of evaporation. Analysis of air mass back trajectories using the HYSPLIT model indicated that precipitation during the dry season was primarily influenced by the continental monsoon, while precipitation during the wet season was affected by both oceanic and continental monsoons. In conclusion, precipitation in the Yellow River Delta is influenced by the evaporation of various water vapor sources, local meteorological factors, and atmospheric water vapor sources, resulting in different isotopic signatures across different scales. The fin-dings would provide a scientific basis for the allocation of scarce water resources in the Yellow River Delta.

Natural condition change and human disturbance can trigger a series of ecological and environmental problems. The construction of ecological network plays a crucial role in balancing contradictions between regional development and ecological protection. We employed the ecological vulnerability assessment method based on pattern-service coupling to construct the ecological network of Ulanqab, Inner Mongolia. The results showed a total area of 12951.2 km² of ecological sources, accounting for 23.8% of the study area, mainly located in the northern part. Additionally, 113 ecological corridors were identified, with a total length of 2397.1 km, which were primarily located in the central and southern parts. Ecological pinch points were mainly situated in the central part of the study area, overlapping with some ecological corridors. Ecological barriers were predominantly distributed around the fragmented ecological sources. Based on the spatial distribution of ecological components within the ecological network, an optimized layout was formed, comprising three zones and one belt (ecological security protection zone, typical grassland restoration zone, natural ecological conservation zone, and ecological corridor development belt). These results provided a scientific theoretical basis for future ecological protection and restoration in this region, which were crucial for maintaining regional ecological security and optimizing the spatial patterns.

Shaanxi Province is an important region for implementing the strategy of ecological conservation and high-quality development of the Yellow River Basin. Based on remote sensing data of vegetation growth, combined with meteorological raster data and digital elevation model data, we used trend analysis, partial correlation analysis, coefficient of variation, residual analysis, and relative impact analysis methods to examine the spatial-temporal varia-tion and driving factors of vegetation growth in the Yellow River Basin of Shaanxi Province during 2001-2020. The results showed that both the normalized difference vegetation index (NDVI) and gross primary productivity (GPP) exhibited a significant upward trend, with a growth rate of 0.066·(10 a)^-1 and 133.610 g C·m^-2·(10 a)^-1, respectively. Spatially, 78.0% and 92.1% of the areas showed significant increases in NDVI and GPP, respectively, with stable vegetation growth in most areas. NDVI and GPP initially decreased and then increased with increasing elevation, and peaking at slopes greater than 20°. Vegetation growth on the shady slope was slightly better than on the sunny slope. Both showed the highest growth rates at elevations of 750-1250 m and slopes of 2°-10°. The NDVI growth rate was greater on the west, southwest, and east slopes, while the GPP change trends were similar across different slope aspects. The areas where NDVI was positively correlated and negatively correlated with ave-rage temperature were approximately equal in size. About 17.0% of the area was significantly positively correlated with precipitation, and 5.6% was significantly negatively correlated with sunshine hours. The spatial distribution of GPP showed significantly positive correlation areas of 6.1% with average temperature and 12.3% with precipitation, with scattered significant correlation areas for sunshine hours. 86.3% of the area showed an improvement in vegetation growth driven by both climate change and human activities. In regions with enhancing vegetation condition, human activities had a relatively positive impact on vegetation growth, accounting for 84.5%, especially in the core areas of the project of returning farmland to forest and grassland. In regions with degradation of vegetation, areas where the relative impact of human activity exceeded 80% accounted for nearly 30%, primarily concentrated in the urban agglomeration of Guanzhong Plain.