Spatio-temporal Evolution of Vegetation and Its Climatic Driving Factors in the Kezhou, Northwest China

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Using preprocessed MOD13Q1 data, we analyzed NDVI, FVC, and NPP trends. Theil-Sen Median trend analysis combined with Mann-Kendall test was employed to examine NPP variation, while Pearson correlation analysis was applied pixel-by-pixel to explore temperature-precipitation-NPP relationships. Results show: (1) NDVI increased at a rate of 0.0021/year, with the mean value in 2011–2020 (0.141) exceeding that of 2000–2010 (0.125), displaying a "high northwest, low southeast" spatial pattern; (2) medium FVC (0.3–0.5) expanded significantly from 10.9% to 17.93%, although medium-low coverage still dominated at 91% in 2020; (3) NPP fluctuated upward initially, rising from 42 g C/m² (2000) to a peak of 51 g C/m² (2015), then declined sharply to 3.8 g C/m² in 2016–2017 (–25.5%) and maintained low levels thereafter, with spatial distribution showing "high north, low south" and 88% of the area exhibiting insignificant trends; (4) precipitation showed positive correlation with NPP across 50.3% of the area, exceeding temperature (43.7%), with central arid basins demonstrating "precipitation-positive, temperature-negative" patterns while the southwestern plateau exhibited warm-humid synergy. These findings indicate that while vegetation coverage achieved continuous improvement, post-2015 NPP dynamics reveal critical insights for sustainable ecosystem management: water availability emerges as the dominant controlling factor for productivity, and the identified climate-vegetation relationships provide a scientific foundation for adaptive strategies to address warming-drying challenges in central basins, thereby supporting targeted ecological restoration and enhanced climate resilience in arid regions. This research contributes to SDG 13 (Climate Action) and SDG 15 (Life on Land) by offering practical guidance for ecosystem restoration and sustainable land management in Belt and Road node areas. Net Primary Productivity (NPP) Normalized Difference Vegetation Index (NDVI) Fractional Vegetation Cover (FVC) climate factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Over the past century, rapid global industrialization has caused massive CO₂ emissions, resulting in the "greenhouse effect" and global warming [ 1 ]. Arid landscapes, dominated by deserts, gobi, and cultivated land [ 2 ], face severe challenges as vegetation serves as a critical bridge connecting ecosystems, atmosphere, soil, and water resources. Under global warming, reduced precipitation and intensified evapotranspiration have led to decreased vegetation coverage and ecosystem degradation. For example, along the edge of the Taklamakan Desert (1999–2008), temperature increases (0.7°C) combined with reduced precipitation caused negative NDVI growth (-0.0068/year) and 8.7% desert expansion [ 3 ]. Currently, arid regions cover approximately 40% of Earth's land surface, contributing 40% of global vegetation Net Primary Productivity (NPP) [ 4 ]. Vegetation NPP and NDVI are key indicators for assessing vegetation ecosystems. The balance of vegetation ecosystems in arid regions is crucial for urban development and ecological security in southern Xinjiang's "Belt and Road" node cities. Systematically analyzing spatio-temporal evolution patterns and identifying climate driving mechanisms are core issues in global change research. The United Nations Sustainable Development Goals (SDGs), particularly SDG 13 (Climate Action), SDG 15 (Life on Land), and SDG 2 (Zero Hunger), emphasize the urgent need to combat climate change, protect terrestrial ecosystems, and ensure food security in vulnerable regions. Northwest China, as an ecologically fragile zone and a critical node of the Belt and Road Initiative, faces severe challenges in balancing economic development with environmental sustainability. Understanding vegetation dynamics and their climatic drivers in this region is essential for formulating adaptive strategies that align with global sustainability agendas and support regional sustainable development. Since the 1980s, space-based remote sensing technology has developed rapidly, offering advantages of continuous spatio-temporal sequences, wide coverage, and low cost in monitoring terrestrial vegetation [ 5 , 6 ]. Scholars increasingly use remote sensing data to study vegetation evolution. Yin Zhenliang et al. found vegetation cover in Northwest China showed an overall increasing trend from 2000 to 2019, with lowest growth in inland arid regions [ 7 ]. Zhang Zhiqiang et al. found significant upward trends in the Yellow River Basin from 2000 to 2020, with obvious improvement in the central basin [ 8 ]. Previous studies [ 9 – 11 ] show terrestrial vegetation in Northwest China's arid regions has grown slowly over the past 20 years. However, differentiated research on NDVI, FVC, and NPP growth rates remains insufficient. This study takes Kezhou, a typical arid region, as the research area, analyzing vegetation ecosystem evolution from three dimensions (NDVI, FVC, NPP) to avoid overlooking situations where external factors produce identical indices with different underlying conditions. Under global warming, research on response mechanisms and spatio-temporal heterogeneity of terrestrial ecosystems in arid regions has become a core scientific issue. However, existing research mostly focuses on single vegetation indices or short-term monitoring, lacking systematic analysis of long-term NPP evolution patterns, particularly at the Pamir-Tianshan junction. The results deepen understanding of carbon cycle processes and climate response mechanisms in arid ecosystems, providing decision-making support for southern Xinjiang's "Belt and Road" development [ 12 ], and theoretical basis for adaptive management in transboundary arid regions. This study constructs a comprehensive framework of "multi-dimensional remote sensing monitoring—geomorphological gradient analysis—climate driving mechanism": First, integrating MODIS and Landsat data, three key parameters (NDVI, FVC, NPP) were extracted for 2000–2020. Second, combined with Mann-Kendall testing, NPP evolution stability was analyzed across geomorphological units. Finally, coupling meteorological data, Pearson correlation and pixel-by-pixel modeling elucidated climate driving mechanisms and spatial non-stationarity. The results deepen understanding of carbon cycle processes and climate response mechanisms in arid ecosystems, contributing to SDG 13 (Climate Action) and SDG 15 (Life on Land) by providing evidence-based insights for sustainable ecosystem management. Furthermore, this research provides decision-making support for southern Xinjiang's "Belt and Road" green development and theoretical basis for adaptive management and transboundary cooperation in arid regions between China and Kyrgyzstan (SDG 17: Partnerships for the Goals). 2 Materials and Methods 2.1 Study Area The Kezhou is situated in southwestern Xinjiang of northwest China at the convergence of the Tianshan Mountains, Pamir Plateau, Kunlun Mountains and Tarim Basin. Covering 72,500 km²with a 1,195 km border shared with Kyrgyzstan and Tajikistan, it comprises one city (Artux) and three counties (Akto, Wuqia, Aheqi). The prefecture has a population of approximately 622,000, predominantly Kyrgyz. Characterized by mountainous terrain exceeding 90% of its area, it features a temperate continental arid climate with scarce precipitation, abundant sunshine and significant diurnal temperature variation, making it a notable fruit-producing region [ 13 ]. 2.2 Data Sources and Preprocessing The remote sensing data used in this study were obtained from the MODIS database. Specifically, the Normalized Difference Vegetation Index (NDVI) data product MOD13Q1 with a continuous time series from 2000 to 2020 was used, with a temporal resolution of 16 days and a spatial resolution of 250 m. For vegetation Net Primary Productivity (NPP), optical remote sensing data including MOD13A1, MOD09A1, MOD15A2H, and MCD17A3 were used (specific parameters are shown in Table 1 ). The climate data were obtained from the National Meteorological Information Center, providing annual mean temperature and precipitation data from 21 meteorological stations in the study area [ 14 ]. Table 1 Usage date and sources Product Type Temporal Resolution Spatial Resolution Data Source MOD13A1/Q1 NDVI 16d 500m/250m https://modis.gsFVC.nasa.gov/ MOD09A1 SR 8d 500 https://modis.gsFVC.nasa.gov/ MOD17A3 LST 1d 1km https://modis.gsFVC.nasa.gov/ MOD15A2H FPAR 8d 1km https://modis.gsFVC.nasa.gov/ Temperature TEM year http://data.cma.cn/ Precipitation PRE year http://data.cma.cn/ 2.3 Methods 2.3.1 Vegetation Fractional Cover Calculation Vegetation Fractional Cover (FVC) is defined as the ratio of vegetation shadow area to total area. This study utilized NDVI data from MOD13Q1 to calculate vegetation fractional cover using the dimidiate pixel model. The calculation formula is as follows [ 15 ]: FVC= \(\:\frac{NDVI-{NDVI}_{S}}{{NDVI}_{V}-{NDVI}_{S}}\) (1) Where FVC represents the fractional vegetation cover (FVC) of Kezhou; NDVI represents the Normalized Difference Vegetation Index of Kezhou; NDVI S represents the normalized vegetation index of pure bare soil pixels in Kezhou, where the minimum NDVI value is used in the calculation; and NDVI V represents the normalized vegetation index of pure vegetation pixels, where the maximum NDVI value is used in the calculation. Since the study area is located in an arid region, the FVC of the study area was classified into five levels based on the classification criteria for vegetation cover in arid areas proposed by Gao Jianjian et al. [ 16 , 17 ]. The detailed classification is presented in Table 2 . Table 2 FVC Type Classification FVC Level FVC Value High cover Higher cover 0.7 ≤ FVC < 1 0.5 ≤ FVC < 0.7 Moderate cover 0.3 ≤ FVC < 0.5 Lower cover 0.1 ≤ FVC < 0.3 Low cover 0 < FVC < 0.1 2.3.2 Vegetation Net Primary Productivity (NPP) Vegetation Net Primary Productivity (NPP) represents the total productivity of terrestrial plants that provides energy for autotrophs and other organisms, and can effectively assess vegetation health status and terrestrial carbon cycling in arid regions. Potter et al. proposed the CASA (Carnegie-Ames-Stanford Approach) model in 1993[ 17 ]. Scholars such as Jiao Wei employed the CASA model to combine remote sensing data, climate data, and plant physiological data, achieving dynamic spatiotemporal simulation of vegetation NPP [ 17 ]. In their research on carbon cycle balance of vegetation resources in the arid regions of Northwest China, Zhang Qifei, Chen Yaning, and others used the CASA model to estimate vegetation NPP in the study area, yielding relatively accurate results [ 18 ]. This study adopts the CASA model methodology, considering factors such as the NDVI index, land change types, temperature (℃), and precipitation (mm) in the study area, and employs two variables—APAR (Absorbed Photosynthetically Active Radiation) and ε (photosynthetic conversion efficiency)—to estimate vegetation NPP. The expression is as follows: $$\:\text{N}\text{P}\text{P}(\text{X},\text{M})=\text{A}\text{P}\text{A}\text{R}(\text{x},\text{m})\times\:{\epsilon\:}(\text{x},\text{m})$$ 2 Where NPP(x, m) represents the net primary productivity of vegetation at pixel x in month m; APAR(x, m) represents the absorbed photosynthetically active radiation at pixel x in month m; and ε(x, m) represents the actual light use efficiency of vegetation at pixel x in month m. 2.3.3 Trend Analysis and Significance Testing The Theil-Sen Median method, abbreviated as Sen trend analysis, offers high computational efficiency and is insensitive to errors in data series, making it commonly used for analyzing long-term time series data trends [ 19 ]. The Mann-Kendall significance test, abbreviated as M-K test, is a non-parametric time series significance trend test frequently employed to examine the significance of long-term variation trends [ 20 ]. By combining the Sen and Mann-Kendall methods to test vegetation NPP change trends, noise interference can be effectively reduced, thereby improving the accuracy and significance of trend detection in time series vegetation NPP [ 21 ]. Sen slope estimation calculates the median of pairwise comparisons within the study area over the study period to investigate the trend of vegetation NPP changes within a specific spatial and temporal context. The calculation formula is as follows: \(\:MI=\text{M}\text{e}\text{d}\text{i}\text{a}\text{n}\left(\frac{{\text{x}}_{\text{j}}-{\text{x}}_{\text{i}}}{\text{j}-\text{i}}\right)\) ,j≤2020, 2000 ≤ i 0 indicates an increasing trend, while MI < 0 indicates a decreasing trend. Median() is the median function, where X i and X j represent the values of the i th and jt h items in the time series, respectively. The magnitude of MI represents the average rate of change[ 22 ]. The M-K method is used to detect significant changes in vegetation NPP in the time series from 2000 to 2020. First, the paired data X i and X j are determined, which are random variables with no fixed increasing or decreasing trend. The test statistic S is shown in formula (4): $$\:\text{S}={\sum\:}_{\text{i}=1}^{\text{n}-1}{\sum\:}_{\text{j}=\text{i}+1}^{\text{n}}\text{s}\text{g}\text{n}({\text{x}}_{\text{j}}-{\text{x}}_{\text{i}})$$ 4 In Eq. ( 5 ), sgn represents the sign function: $$\:\text{s}\text{g}\text{n}\left({\text{x}}_{\text{j}-}{\text{x}}_{\text{i}}\right)=\left\{\begin{array}{c}+1,{\text{x}}_{\text{j}-}{\text{x}}_{\text{i}}>0\\\:0,{\text{x}}_{\text{j}-}{\text{x}}_{\text{i}}=0\\\:-1,{\text{x}}_{\text{j}-}{\text{x}}_{\text{i}}0\\\:0,s=0\\\:\frac{\text{S}+1}{\sqrt{\text{v}\text{a}\text{r}\left(\text{s}\right)}},s<0\end{array}\right.$$ 6 In Eq. ( 7 ), var(s) is the variance: $$\:\text{v}\text{a}\text{r}\left(\text{s}\right)=\frac{\text{n}(\text{n}-1)(2\text{n}+5)}{18}$$ 7 2.3.4 Pearson Correlation Analysis Pearson Correlation Analysis is used to examine the correlation relationship between two variables, i.e., predicting one variable based on another [ 23 – 24 ]. In this study, the Pearson correlation method was employed to calculate the correlation between temperature, precipitation, and vegetation NPP on a pixel-by-pixel basis. Specifically, raster data of temperature and precipitation with the same pixel size as vegetation NPP were generated year by year in GIS software using the inverse distance weighting (IDW) interpolation method, and the correlation between temperature, precipitation, and NPP was calculated using Matlab software. The calculation formula is as follows: $$\:\:Re=\frac{{\sum\:}_{i=1}^{n}(X-\stackrel{-}{X})(Y-\stackrel{-}{Y})}{\sqrt[2]{{\sum\:}_{i=1}^{n}{X}^{2}\sum\:_{i=1}^{n}{Y}^{2}}}$$ 8 Where (9) Where X i represents the value of the vegetation index (NDVI, NPP) in year i ; represents the 20-year mean value of the vegetation index; Y i represents the value of the correlating variable (temperature, precipitation) in year i; andrepresents the 20-year mean value of the correlating variable. Re ranges between [-1, 1]. According to the correlation classification criteria proposed by Chen Chunbo [ 25 – 26 ] and other scholars, the classification adopted in this station adopted in this study is shown in Table 3 : Table 3 Re judgement criteria Re Type Re Classification Criterion Re Type Correlation Trend High correlation ⎢Re⎢ ≥ 0.8 Re > 0 Positive correlation Moderate correlation 0.5 ≤ ⎢Re⎢<0.8 Low correlation 0.2 ≤ ⎢Re⎢<0.5 Re < 0 Negative correlation No correlation ⎢Re⎢<0.2 3 Results and Analysis 3.1 Spatio-temporal Evolution Characteristics of Vegetation Index NDVI 3.1.1 Temporal Evolution Characteristics and Trends of Vegetation NDVI From a temporal perspective, based on the spatio-temporal evolution characteristics of the vegetation growing season NDVI index in the study area from 2000 to 2020 (Fig. 2 ), the average NDVI index across the entire region fluctuated between 0.12 and 0.16 over the past 20 years, with an overall increasing rate of 0.0021. The 20-year mean NDVI value was 0.132. Specifically, the mean vegetation NDVI from 2000 to 2010 was 0.125, while the average NDVI from 2011 to 2020 was 0.141, representing a year-on-year increase of 15.3% (compared to the 2000–2010 mean). During the period 2000–2010, the annual mean vegetation NDVI in Kezhou showed positive year-on-year growth in three years (2002, 2006, and 2009), while the remaining years exhibited negative growth. During the period 2011–2020, the annual mean vegetation NDVI showed positive growth in six years (2010, 2012, 2015, 2016, 2017, and 2020) and negative growth in three years. The annual mean vegetation NDVI during the latter decade was significantly higher than that of the previous decade (2000–2010). As shown by the variation trend of the annual mean NDVI in Kezhou over the 20-year period (Figure 3 ), the annual mean NDVI in the Kezhou fluctuated within the range of 0.16–0.22, exhibiting an overall slow upward trend. During the period 2000–2010, the annual mean NDVI increased from approximately 0.16 to 0.19, representing an increase of approximately 18.8%; during the period 2011–2020, the annual mean NDVI increased from approximately 0.19 to 0.21, representing an increase of approximately 10.5%, indicating a deceleration in the growth rate during the latter period. 3.1.2 Spatial Distribution Characteristics of Vegetation NDVI in Kezhou Kezhou's average NDVI spatial distribution (2000–2020) exhibits significant differentiation: high in the northwest, low in the southeast, with strip patterns along water systems and oases. High NDVI areas (0.312–0.739) concentrate in the piedmont oasis belt at the southern Tianshan Mountains, northeastern river valleys, and intermountain basins. Relying on stable snow/ice meltwater and rivers ( Kezhou, Gaizi), these form the main agricultural zones and natural oases with NDVI above 0.458, some exceeding 0.7, demonstrating a distinct "oasis effect." Medium NDVI regions (0.131–0.312) occur at oasis peripheries, river terraces, and low mountain hilly areas as ecological transition zones dominated by grasslands, shrubs, and dryland farming with patchy distributions. Low NDVI areas (0.008–0.131) cover southern and central regions including the Taklamakan Desert, Gobi, and bare rock, with sparse desert vegetation generally below 0.131. Water resources constitute the core determinant of NDVI patterns, while topography regulates distribution through precipitation and meltwater convergence. Human activities significantly influence spatial distribution—agricultural irrigation areas display higher NDVI than surrounding natural deserts, reflecting artificial oasis enhancement, whereas scattered desert-edge oases show lower coverage due to limited water. Overall, NDVI distribution reflects both arid region ecosystem vulnerability and the regulatory roles of water resources and human activities. 3.2 Spatiotemporal Evolution Characteristics of Fractional Vegetation Cover (FVC) in Kezhou Figure 5 illustrates the spatial distribution patterns and dynamic change characteristics of Fractional Vegetation Cover (FVC) in the study area for four periods: 2000, 2005, 2015, and 2020. The study area shows a spatial pattern of "high in the northwest and southeast, low in the southwest," with high vegetation coverage concentrated in northwestern mountainous regions and low coverage in southeastern plains and urban agglomerations. From 2000 to 2020, vegetation coverage exhibited an overall improving trend, characterized by continuous expansion of high and sub-high FVC areas and gradual reduction of low and sub-low FVC areas. Improvement was modest during 2000–2005 with minimal spatial pattern changes, while 2005–2015 witnessed the most significant improvement, with green areas expanding southeastward, indicating positive effects from ecological restoration projects or climatic factors. The bar chart reveals significant structural changes in vegetation coverage from 2001 to 2020, showing an overall optimization trend of "decreasing low coverage grades and increasing medium-high coverage grades." Relatively low vegetation coverage remained dominant, fluctuating and declining from approximately 82% in 2001 to 74% in 2005, rebounding to 80% in 2010, slightly decreasing to 79% in 2015, then substantially increasing to 91% in 2020. Low vegetation coverage showed a consistent downward trend from 7% to 2%, reflecting significant ecological improvement. Moderate vegetation coverage peaked at 13% in 2005, fluctuating between 8%–11% in other years. Both relatively high and high vegetation coverage exhibited slow upward trends, increasing from 2% to 6% and 1% to 3% respectively, indicating gradual expansion of high-grade vegetation areas. In summary, vegetation coverage in the region transformed from "medium-low coverage dominance with relatively high low coverage proportion" to "absolute dominance of medium-low coverage with gradual high coverage development," demonstrating stable and positive ecological environmental quality improvement. 3.3 Spatio-temporal Evolution Characteristics of Vegetation NPP in Kezhou 3.3.1 Temporal Distribution Characteristics of Vegetation NPP in Kezhou This line chart illustrates the inter-annual variation trend of vegetation Net Primary Productivity (NPP) in the Kezhou from 2000 to 2020. The figure clearly reveals that over the past 21 years, vegetation NPP in this region has undergone a three-stage evolution process of "fluctuating increase—sharp decline—low-level fluctuation," exhibiting an overall inverted "V"-shaped pattern characterized by initial growth followed by decline. Specifically, during the period 2000–2015, vegetation NPP demonstrated an overall fluctuating upward trend, gradually increasing from approximately 42 g C/m² in 2000. This period witnessed multiple minor fluctuations, including brief declines during 2003–2004, adjustments during 2007–2008, and further fluctuations during 2011–2012. Nevertheless, the overall upward trend remained evident, reaching a peak of approximately 51 g C/m² in 2015. The average annual growth rate during this stage was approximately 0.06 g C/m², reflecting continuous improvement in regional vegetation productivity, which may be closely associated with the implementation of ecological restoration projects, climate warming and humidification, and other related factors. 3.3.2 Spatial Evolution Trends and Significance Testing of Vegetation NPP in Kezhou NPP spatial distribution (2000–2020) exhibits significant heterogeneity characterized by "high in the north and low in the south, high in the east and low in the west, high in mountainous areas and low in plains." High NPP areas (green) concentrate in northern and northeastern mountainous regions in strip distributions, reaching above 57.83 g C/m² with alpine meadows and forests showing strong photosynthetic capacity. Two distinct high NPP zones around water bodies appear in the east-central part, displaying "water body-oasis" synergistic characteristics of irrigated agriculture or riparian vegetation. Medium-high NPP areas (yellow-green) appear patchily in northwestern mountains, while vast southwest and southeast plains are dominated by low NPP areas (dark red) approaching 0 g C/m², indicating sparse vegetation constrained by extreme aridity, poor soil, or intensive human activities. NPP grades present gradient succession from north to south and mountains to plains, with topography playing a decisive role. Low NPP areas occupy absolute advantage in total area while high NPP areas show island-like distribution, indicating relatively low overall productivity and fragile ecological baseline conditions. Despite inter-annual fluctuations, the spatial pattern remained stable, coupled with topographic, climatic, and hydrological differentiation. Significance testing reveals insignificant increase (yellow-green) dominates approximately 85–90%, extensively distributed across south-central plains, eastern mountains, and western areas, indicating stable or slowly improving ecosystems. Significant increase areas (dark red, ~ 5–8%) concentrate in northwestern and central-northern mountainous zones with statistically reliable improvement. Insignificant decrease areas (blue, ~ 4–6%) embed within northeastern high NPP baseline areas, while significant decrease (white) is extremely rare (~ 0–1%). Notably, significant increase areas mainly locate in medium NPP baseline areas whereas high NPP baseline areas show insignificant trends, implying ecological restoration achieved more significant results in moderately conditioned areas. Overall, vegetation NPP changes were primarily characterized by insignificant increase with significant improvement concentrated in northwestern and northern mountainous regions, demonstrating a stable and positive development trend. Table 4 Re judgement criteria Type of Change Classification Standard Area Percentage (%) Insignificant decrease S ≤ 0,Z ≥ 1.96 5 Significant decrease S < 0, Z < 1.96 0.5 Insignificant increase S ≥ 0,Z 0, Z > 1.96 6.5 4 Analysis of Climate Change Drivers of Vegetation NPP This study employed Pearson correlation analysis between annual mean climate factors (temperature and precipitation, 2000–2020) and 20-year mean NPP via pixel-by-pixel calculations. Temperature-NPP correlation ranged from − 0.792 to 0.875, with positive correlations covering 43.7% of the area, mainly in high population density zones and southwestern plateau regions (blue areas in precipitation figure). This indicates human interventions (irrigated agriculture) or growing season extension in high-altitude cold environments promoted productivity. Negative temperature correlations occupied 23% (large red regions in temperature figure), where warming intensified evapotranspiration in central arid watersheds, causing vegetation water stress and decreased productivity due to insufficient precipitation compensation. Precipitation-NPP correlation ranged from − 0.81 to 0.86, with positive correlations covering 50.3% (exceeding temperature) and negative correlations at 19.2%. Central arid regions exhibited "precipitation-positive dominance with temperature-negative correlation," indicating water availability as the core factor controlling vegetation productivity, while warming negatively affected productivity through enhanced evapotranspiration. The southwestern plateau showed positive correlations with both factors (blue in both figures), where temperature increases promoted alpine snow/ice melt, increasing river runoff and irrigation water, forming a "warm-humid" synergistic promotion contrasting with the "warm-dry" stress in central watersheds. Overall, vegetation NPP response to climate factors exhibits significant spatial heterogeneity, with water availability as the key explanatory variable. The combined effects of water-heat conditions, topography, and human activities shaped this complex spatial pattern. 5 Discussion 5.1 Analysis of Spatiotemporal Evolution Characteristics of Vegetation Index NDVI Kezhou's NDVI increased at 0.0021/year from 2000–2020, showing slow improvement consistent with vegetation growth trends in Northwest China but below the regional average, reflecting restoration difficulties in extreme arid zones. Temporally, mean NDVI during 2011–2020 (0.141) increased 15.3% compared to 2000–2010 (0.125), with positive growth years rising from 3 to 6, indicating significantly improved vegetation trends in the recent decade, likely related to continued implementation of ecological projects like "Grain for Green." Spatially, NDVI presents a "high northwest, low southeast" pattern distributed along water systems and oases, consistent with research on Xinjiang's ecological vulnerability. High-value areas concentrate in the Tianshan Mountains' southern piedmont oasis belt and river valleys, exceeding 0.458 and demonstrating the "oasis effect," while southern Taklamakan Desert edges remain below 0.131. This differentiation is determined by hydrothermal conditions: northwestern mountains receive abundant precipitation from orographic lifting plus snow/ice meltwater, whereas southeastern plains suffer severe water stress from scarce precipitation and intense evaporation. Notably, Kezhou's NDVI growth rate (0.18 × 10⁻²/year) is significantly lower than the Yellow River basin and Aksu region, closely related to its "three mountains sandwiching two valleys" topography. Mountains exceed 90% of the area with limited usable oasis and fragile ecosystems, resulting in slow vegetation restoration. 5.2 Evolution Characteristics and Ecological Significance of Fractional Vegetation Cover (FVC) FVC classification results reveal that medium-coverage vegetation expansion was most significant from 2000–2020, increasing from 10.9% to 17.93% (64.4% growth), becoming the main contributor to vegetation cover structure optimization—similar to Loess Plateau research findings, indicating that arid area restoration first manifests as medium-coverage region expansion. Low-coverage areas decreased from 7% to 2%, demonstrating significantly improved ecological conditions and alleviated desertification. However, high-coverage and relatively high-coverage proportions remain low (~ 9% combined in 2020) with slow growth, indicating vegetation quality remains at medium-to-low levels. In 2020, medium-low coverage accounted for 91%, showing the vegetation body remains sparse vegetation and grasslands with simple ecosystem structure and insufficient stability. Future restoration should focus on improving cover quality and promoting conversion from low to medium-high coverage. Spatially, 2005–2015 witnessed the fastest FVC improvement with southeastward green expansion, consistent with rising NPP trends and reflecting superimposed effects of ecological projects and climate warming/humidification. After 2015, improvement slowed and stabilized, possibly related to ecosystem equilibrium or intensified water resource constraints. 5.3 Spatio-temporal Evolution of Vegetation NPP and Climate Driving Mechanisms Kezhou's vegetation NPP exhibited a three-stage evolution: "fluctuating increase—sharp decline—low-level fluctuation." After peaking in 2015 (~ 5.1 g C/m²), it experienced a cliff-like decline, maintaining low levels of 3.8–3.9 g C/m² during 2017–2020. This contrasts with continuous FVC improvement, revealing asynchrony between vegetation coverage (quantity) and productivity (quality). The sharp NPP decline may be attributed to: (1) Climatic anomalies: Periodic drought after 2015 featured decreasing precipitation and rising temperatures, intensifying evapotranspiration and vegetation water stress. Temperature shows significant negative correlation with NPP in central arid watersheds, differing from conclusions that temperature has minimal effects on Xinjiang grassland NPP. (2) Water-heat combination effects: The southwestern plateau shows positive correlation with both factors (warming promotes snow/ice melt, increasing runoff), forming a "warm-humid" synergistic effect. Conversely, central watersheds exhibit "warm-dry" stress, with precipitation-positive areas (50.3%) exceeding temperature-positive areas (43.7%), indicating water availability as the core productivity control factor. (3) Ecosystem threshold effects: Continuous increase before 2015 may have approached or exceeded local water resource carrying capacity thresholds, causing subsequent productivity decline—suggesting ecological thresholds exist for arid region restoration. (4) Significance testing shows 88% of the region exhibited insignificant NPP increases, with only 6.5% significant increases located in medium NPP baseline areas rather than high baseline areas. This "middle breakthrough" effect implies ecological projects achieve better results in moderately conditioned areas, providing reference for differentiated restoration strategies. 5.4 Spatial Heterogeneity of Climate Factor Driving Mechanisms Pearson correlation analysis reveals significant spatial heterogeneity in Kezhou's vegetation NPP response to climate factors. (1) Dual temperature effects: Positive correlations (43.7%) occur in densely populated areas (irrigated agriculture heat utilization) and the southwestern plateau (growing season extension in high-altitude cold environments). Negative correlations (23%) concentrate in central arid watersheds where warming intensifies evapotranspiration and insufficient precipitation causes water stress. (2) Precipitation dominance: Positive correlation areas (50.3%) exceed temperature, mainly in the southwestern plateau and major river basins, indicating moisture as the primary growth-limiting factor—consistent with Chen Chunbo et al.'s Xinjiang grassland conclusions. Negative correlation areas (19.2%) occur in the Tarim Basin central desert and Gobi, where increased precipitation may accompany extreme weather or fail to convert to available moisture, potentially triggering runoff erosion. 5.5 Research Limitations and Prospects This study has limitations: (1) CASA model estimation didn't fully consider soil types, CO₂ effects, and human management practices, potentially biasing agricultural estimates; (2) uneven meteorological station distribution, particularly sparse coverage in northwestern mountains, introduces interpolation uncertainty; (3) only temperature and precipitation were analyzed, excluding solar radiation, wind speed, and human activities. Future research should: (1) integrate multi-source remote sensing (Landsat, Sentinel-2) to improve spatial resolution; (2) employ Geodetector and multiple regression to quantitatively distinguish climate and human contributions; (3) couple NPP with ecosystem service values and carbon sequestration to support "dual carbon" goals. 5.6 Implications for Sustainable Development This study provides critical insights for achieving multiple Sustainable Development Goals (SDGs) in arid regions, particularly in the context of climate change and ecosystem restoration. SDG 13 (Climate Action): Our findings reveal the asymmetric response of vegetation productivity to climate warming in Kezhou, with central watersheds experiencing "warm-dry" stress while the southwestern plateau benefits from "warm-humid" conditions. This spatial heterogeneity highlights the need for climate adaptation strategies that account for local geomorphological gradients rather than uniform regional policies. The identification of ecosystem threshold effects—where NPP collapsed after 2015 despite continued greening—demonstrates the vulnerability of arid ecosystems to climatic extremes, underscoring the urgency of climate action in these fragile environments. The precipitation-dominant control (50.3% positive correlation) over vegetation productivity emphasizes that water resource management is central to climate adaptation in arid regions. SDG 15 (Life on Land): The dominance of medium-low vegetation coverage (91% in 2020) and the low proportion of high-coverage areas (3%) indicate that Kezhou's ecosystem remains fragile with simple vegetation structure. Our recommendation for "water-limited greening" and differentiated restoration strategies for medium-coverage areas directly supports the UNCCD's Land Degradation Neutrality targets. The expansion of medium FVC (64.4% growth from 10.9% to 17.93%) demonstrates progress in combating desertification, but the stagnation of high-coverage vegetation and the sharp NPP decline after 2015 suggest that achieving SDG 15's ambition of "protecting, restoring and promoting sustainable use of terrestrial ecosystems" requires long-term commitment and quality-oriented rather than merely coverage-oriented restoration approaches. 6 Conclusions Based on MODIS and meteorological data (2000–2020), this study employed Theil-Sen, Mann-Kendall, and Pearson correlation analyses to reveal spatio-temporal vegetation evolution and climate drivers in Kezhou. Key findings: (1) NDVI increased at 0.0021/year, with improvement accelerating post-2011. Spatially, it shows "high northwest, low southeast" patterns along water systems, with high values (> 0.458) in Tianshan piedmont oases and low values (< 0.131) in southern deserts. (2) FVC structure optimized: medium coverage expanded from 10.9% to 17.93%, low coverage decreased from 7% to 2%, though high coverage remains low (3%) and medium-low coverage dominates at 91%. (3) NPP peaked at 5.1 g C/m² in 2015 then declined 25.5% to 3.8 g C/m², showing "rise then fall" trends with 88% of areas exhibiting insignificant increases. (4) Climate responses show spatial heterogeneity: precipitation-positive areas (50.3%) exceed temperature (43.7%), with central watersheds showing "warm-dry" stress and southwestern plateau showing "warm-humid" synergy. (5) The ecosystem is improving but vulnerable, particularly to warming-drying trends. Recommendations include water-limited greening, differentiated strategies for medium-coverage areas, and cross-border water cooperation. Declarations Author Contributions: Wang Jianping wrote the original manuscript; Aynur Mamat and Aida Orozobekova designed the experimental method; Almazbek Arzybaev revised the paper. All authors have read and agreed to the published version of the manuscript. Funding: No Fundings . Acknowledgments: We would like to thank the anonymous reviewers for their constructive com- ments that helped to improve the quality of this manuscript. Consent to Publish declaration: not applicable Consent to Participate declaration: not applicable Ethics declaration: not applicable Conflicts of Interest: The authors declare no conflict of interest. References Gaur S, Mittal A, Bandyopadhyay A, et al. Spatio-temporal analysis of land use and land cover change: A systematic model inter-comparison driven by integrated modelling techniques. Int J Remote Sens. 2020;41(23):9229–55. Bufebo B, Elias E. Land use/land cover change and its driving forces in Shenkolla Watershed, South Central Ethiopia. Sci World J. 2021;2021:9470918. https://doi.org/10.1155/2021/9470918 . Msofe NK, Sheng LX, Lyimo J. Land use change trends and their driving forces in the Kilombero Valley Floodplain, Southeastern Tanzania. Sustainability. 2019;11(2):505. https://doi.org/10.3390/su11020505 . Hazem T, El-Hamid A. Geospatial analyses for assessing the driving forces of land use/land cover dynamics around the nile delta branches, Egypt. J Indian Soc Remote Sens. 2020;48(12):1–14. Li SH, Jin BX, Zhou JS, et al. Analysis of the spatiotemporal land-use/land-cover change and its driving forces in Fuxian Lake Watershed, 1974 to 2014. Pol J Environ Stud. 2017;26(2):671–81. Cléber da Silva-Vieira C, Ferrari E, Ruiz C. Sustainable perceptions of climate actions in universities: a bibliometric review. Discov Sustain. 2026;7:216. https://doi.org/10.1007/s43621-025-02506-w . Xiujuan S, Yanhong L, Ende F, et al. Research on the impact of the digital economy on high-quality development of China's new urbanization. Discov Sustain. 2026. https://doi.org/10.1007/s43621-026-02906-6 . Zhang SJ, Zhou C, Xiao TQ et al. Spatiotemporal coupling and influencing factors of new urbanization and carbon emissions in the Yangtze River Economic Belt: Based on XGBoost-SHAP model. Environ Sci 2025;1–19. Zhou J, Zhang HQ. Supporting the high-quality development of new urbanization with advanced green and low-carbon technologies. Macroeconomic Manage. 2025;2025(03):56–62. Huang J, Lu H, Du M. Regional Differences in Agricultural Carbon Emissions in China: Measurement, Decomposition, and Influencing Factors. Land. 2025;14(4):682–682. Zhang J, Fu K, Liu BR. How does the digital economy empower urban low-carbon transformation: from the perspective of dual target constraints. Mod Finance Economics-Journal Tianjin Univ Finance Econ. 2022;2022(8):3–23. Feng ZY, Song DL, Xie WS. Digital economy helping to achieve the double carbon goals: basic approaches, internal mechanisms and action strategies. J Beijing Normal Univ (Social Sci Edition). 2023;2023(1):5. Ahemaiti TABANA, Jian-bo GU, Maimaiti AYINUER, et al. 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Pol J Environ Stud. 2024;33(5):5249–62. https://doi.org/10.15244/pjoes/182893 . Zhang L, Yan H, Qiu L, et al. Spatial and temporal analyses of vegetation changes at multiple time scales in the Qilian Mountains. Remote Sens. 2021;13(24). https://doi.org/10.3390/rs13245046 . Zuo Y, Li Y, He K, et al. Temporal and spatial variation characteristics of vegetation coverage and quantitative analysis of its potential driving forces in the Qilian Mountains, China, 2000–2020. Ecol Ind. 2022;143:109429. https://doi.org/10.1016/j.ecolind.2022.109429 . Zhang Z, Huo L, Su Y, et al. Estimation of corn net primary productivity and carbon sequestration based on the CASA model: A case study of the Fen River Basin. Sustainability. 2024;16(7). https://doi.org/10.3390/su16072942 . Zhang J, Wang J, Chen Y, et al. Spatiotemporal variation and prediction of NPP in Beijing-Tianjin-Hebei region by coupling PLUS and CASA models. Ecol Inf. 2024;81:102620. https://doi.org/10.1016/j.ecoinf.2024.102620 . Liu J, Shen L, Chen Z, et al. Assessing the response of the net primary productivity to snow phenology changes in the Tibetan Plateau: trends and environmental drivers. Remote Sens. 2024;16(19). https://doi.org/10.3390/rs16193566 . Jiang H, Lin J, Liu B, et al. Discovering the ecosystem service value growth characteristics of a subtropical soil erosion area using a remote-sensing-driven mountainous equivalent factor method. Remote Sens. 2024;16(19). https://doi.org/10.3390/rs16193700 . Zhang J, Zhang Y. Quantitative assessment of the impact of the three-north shelter forest program on vegetation net primary productivity over the past two decades and its environmental benefits in China. Sustainability. 2024;16(9). https://doi.org/10.3390/su16093656 . Hurst HE. Long-term storage capacity of reservoirs. Trans Am Soc Civ Eng. 1951;116(1):800–3. Chen CB, Li GY, Peng J. Spatio-temporal characteristics of Xinjiang grass land NDVI and its response to climate change from 1981 to 2018. Acta Ecol Sin. 2023;43(4):1537–52. He J, Yang K, Tang WJ, Lu H, Qin J, Chen YY, Li X. The first high-resolution meteorological forcing data set for land process studies over China. Sci Data. 2020;7:25. Li C, Wang RH, Ning HS, Luo QH. Characteristics of meteorological drought pattern and risk analysis for maize production in Xinjiang, Northwest China. Theoret Appl Climatol. 2018;133(3):1269–78. Wang Q, Zhai PM, Qin DH. New perspectives on 'warming-wetting' trend in Xinjiang, China. Adv Clim Change Res. 2020;11(3):252–60. Yao JQ, Chen YN, Zhao Y, Mao WY, Xu XB, Liu Y, Yang Q. Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China. Theoret Appl Climatol. 2018;131(3/4):1503–15. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviewers agreed at journal 14 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 11 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Aitmatov Ave, Bishkek 720044, Kyrgyz Republic","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Jianping","suffix":""},{"id":623751914,"identity":"86067cad-e49c-4469-9271-6091abfc21ad","order_by":1,"name":"Almazbek Arzybaev","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYJACZhDBzwNmJ5CgRbKHgbGBNC0GZ4jVwj/7+DPpghqbxM1njj9/zMOQZtdASIvEuYQ06RnH0hK3ne0xbOZhyEkmqIXhDMMxaR62w8Zm53kYgVoqkgnqkD/D2CbN8++/sXE/+0PitBicYWaT5m07IGfA2wB2mB1BLYZn2JitZ/Yly0mcOWM4c45BWgJBLXJn2B/eLvhmx8Pfk/7gw5uKZHuCWoCARQLJnQyJDURoYf6AzCPKllEwCkbBKBhZAACH8jh8Dv+1OQAAAABJRU5ErkJggg==","orcid":"","institution":"Applied Informatics Department of Kyrgyz State Technical University named after I. Razzakov, 66 Ch. 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09:20:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108498,"visible":true,"origin":"","legend":"\u003cp\u003eThe average and year-on-year changes of NDVI in the Kezhou\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/5e1745762b1e2091608d1925.png"},{"id":107705849,"identity":"4436a39f-faa0-4e94-adaf-167d7394aea3","added_by":"auto","created_at":"2026-04-24 09:15:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81760,"visible":true,"origin":"","legend":"\u003cp\u003eThe Trend of NDVI annual average change in Kezhou\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/4f6ceae711c162a5c418f870.png"},{"id":107707294,"identity":"15754d49-eb48-427c-b5d8-00048223ca72","added_by":"auto","created_at":"2026-04-24 09:20:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":353690,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution map of annual average NDVI in the Kezhou (2000–2020)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/e8315bbb570a5c9775833da6.png"},{"id":107631633,"identity":"6f590765-eef2-4777-bd91-c32c9817e4d0","added_by":"auto","created_at":"2026-04-23 11:52:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":601563,"visible":true,"origin":"","legend":"\u003cp\u003eFVC spatial distribution of vegetation in Kezhou, 2000, 2005，2010, 2015, 2020\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/408af8e380bbb5f2e605a601.png"},{"id":107631634,"identity":"8f61af52-482a-4356-b8ca-9e58875f76e0","added_by":"auto","created_at":"2026-04-23 11:52:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93292,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual variation of FVC in the Kezhou\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/1cda66dfd5b99ca9ac37e17c.png"},{"id":107707458,"identity":"1adc6622-5114-464a-8a17-1aef91e41bc2","added_by":"auto","created_at":"2026-04-24 09:20:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":23492,"visible":true,"origin":"","legend":"\u003cp\u003eThe inter-annual variation trend of vegetation NPP in the Kezhou\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/ffa440b0947955bde8497dbf.png"},{"id":107707416,"identity":"61dcc7d8-515b-4688-af05-e701c2d414a3","added_by":"auto","created_at":"2026-04-24 09:20:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":243743,"visible":true,"origin":"","legend":"\u003cp\u003eMean distribution of vegetation NPP in the Kezhou\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/d35b520b8ba6dbfaf58228b8.png"},{"id":107631637,"identity":"8c58cdc2-144f-4056-bfdd-fe34f5379c88","added_by":"auto","created_at":"2026-04-23 11:52:28","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":198612,"visible":true,"origin":"","legend":"\u003cp\u003eTrends and significance test distribution of vegetation NPP changes in the Kezhou\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/073725f8713d85f762777dba.png"},{"id":107707232,"identity":"5567edc4-2584-4f41-a7a1-c871359f218c","added_by":"auto","created_at":"2026-04-24 09:19:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":558115,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between climate factors and vegetation NPP\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/1b6c2d5827d7be4c3b8231d9.png"},{"id":107709339,"identity":"9ebf927d-d70f-4dd7-9719-5c51c6e67551","added_by":"auto","created_at":"2026-04-24 09:35:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2600371,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9094169/v1/f31b0b24-f7f6-4bf5-9254-c2d7bd3c6dd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatio-temporal Evolution of Vegetation and Its Climatic Driving Factors in the Kezhou, Northwest China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOver the past century, rapid global industrialization has caused massive CO₂ emissions, resulting in the \"greenhouse effect\" and global warming [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Arid landscapes, dominated by deserts, gobi, and cultivated land [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], face severe challenges as vegetation serves as a critical bridge connecting ecosystems, atmosphere, soil, and water resources. Under global warming, reduced precipitation and intensified evapotranspiration have led to decreased vegetation coverage and ecosystem degradation. For example, along the edge of the Taklamakan Desert (1999\u0026ndash;2008), temperature increases (0.7\u0026deg;C) combined with reduced precipitation caused negative NDVI growth (-0.0068/year) and 8.7% desert expansion [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, arid regions cover approximately 40% of Earth's land surface, contributing 40% of global vegetation Net Primary Productivity (NPP) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Vegetation NPP and NDVI are key indicators for assessing vegetation ecosystems. The balance of vegetation ecosystems in arid regions is crucial for urban development and ecological security in southern Xinjiang's \"Belt and Road\" node cities. Systematically analyzing spatio-temporal evolution patterns and identifying climate driving mechanisms are core issues in global change research. The United Nations Sustainable Development Goals (SDGs), particularly SDG 13 (Climate Action), SDG 15 (Life on Land), and SDG 2 (Zero Hunger), emphasize the urgent need to combat climate change, protect terrestrial ecosystems, and ensure food security in vulnerable regions. Northwest China, as an ecologically fragile zone and a critical node of the Belt and Road Initiative, faces severe challenges in balancing economic development with environmental sustainability. Understanding vegetation dynamics and their climatic drivers in this region is essential for formulating adaptive strategies that align with global sustainability agendas and support regional sustainable development.\u003c/p\u003e \u003cp\u003eSince the 1980s, space-based remote sensing technology has developed rapidly, offering advantages of continuous spatio-temporal sequences, wide coverage, and low cost in monitoring terrestrial vegetation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Scholars increasingly use remote sensing data to study vegetation evolution. Yin Zhenliang et al. found vegetation cover in Northwest China showed an overall increasing trend from 2000 to 2019, with lowest growth in inland arid regions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Zhang Zhiqiang et al. found significant upward trends in the Yellow River Basin from 2000 to 2020, with obvious improvement in the central basin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] show terrestrial vegetation in Northwest China's arid regions has grown slowly over the past 20 years. However, differentiated research on NDVI, FVC, and NPP growth rates remains insufficient. This study takes Kezhou, a typical arid region, as the research area, analyzing vegetation ecosystem evolution from three dimensions (NDVI, FVC, NPP) to avoid overlooking situations where external factors produce identical indices with different underlying conditions.\u003c/p\u003e \u003cp\u003eUnder global warming, research on response mechanisms and spatio-temporal heterogeneity of terrestrial ecosystems in arid regions has become a core scientific issue. However, existing research mostly focuses on single vegetation indices or short-term monitoring, lacking systematic analysis of long-term NPP evolution patterns, particularly at the Pamir-Tianshan junction.\u003c/p\u003e \u003cp\u003eThe results deepen understanding of carbon cycle processes and climate response mechanisms in arid ecosystems, providing decision-making support for southern Xinjiang's \"Belt and Road\" development [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and theoretical basis for adaptive management in transboundary arid regions. This study constructs a comprehensive framework of \"multi-dimensional remote sensing monitoring\u0026mdash;geomorphological gradient analysis\u0026mdash;climate driving mechanism\": First, integrating MODIS and Landsat data, three key parameters (NDVI, FVC, NPP) were extracted for 2000\u0026ndash;2020. Second, combined with Mann-Kendall testing, NPP evolution stability was analyzed across geomorphological units. Finally, coupling meteorological data, Pearson correlation and pixel-by-pixel modeling elucidated climate driving mechanisms and spatial non-stationarity. The results deepen understanding of carbon cycle processes and climate response mechanisms in arid ecosystems, contributing to SDG 13 (Climate Action) and SDG 15 (Life on Land) by providing evidence-based insights for sustainable ecosystem management. Furthermore, this research provides decision-making support for southern Xinjiang's \"Belt and Road\" green development and theoretical basis for adaptive management and transboundary cooperation in arid regions between China and Kyrgyzstan (SDG 17: Partnerships for the Goals).\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe Kezhou is situated in southwestern Xinjiang of northwest China at the convergence of the Tianshan Mountains, Pamir Plateau, Kunlun Mountains and Tarim Basin. Covering 72,500 km\u0026sup2;with a 1,195 km border shared with Kyrgyzstan and Tajikistan, it comprises one city (Artux) and three counties (Akto, Wuqia, Aheqi). The prefecture has a population of approximately 622,000, predominantly Kyrgyz. Characterized by mountainous terrain exceeding 90% of its area, it features a temperate continental arid climate with scarce precipitation, abundant sunshine and significant diurnal temperature variation, making it a notable fruit-producing region [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources and Preprocessing\u003c/h2\u003e \u003cp\u003eThe remote sensing data used in this study were obtained from the MODIS database. Specifically, the Normalized Difference Vegetation Index (NDVI) data product MOD13Q1 with a continuous time series from 2000 to 2020 was used, with a temporal resolution of 16 days and a spatial resolution of 250 m. For vegetation Net Primary Productivity (NPP), optical remote sensing data including MOD13A1, MOD09A1, MOD15A2H, and MCD17A3 were used (specific parameters are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The climate data were obtained from the National Meteorological Information Center, providing annual mean temperature and precipitation data from 21 meteorological stations in the study area [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUsage date and sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemporal Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOD13A1/Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500m/250m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modis.gsFVC.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://modis.gsFVC.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOD09A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modis.gsFVC.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://modis.gsFVC.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOD17A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modis.gsFVC.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://modis.gsFVC.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOD15A2H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFPAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://modis.gsFVC.nasa.gov/\u003c/span\u003e\u003cspan address=\"https://modis.gsFVC.nasa.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.cma.cn/\u003c/span\u003e\u003cspan address=\"http://data.cma.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eyear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://data.cma.cn/\u003c/span\u003e\u003cspan address=\"http://data.cma.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Vegetation Fractional Cover Calculation\u003c/h2\u003e \u003cp\u003eVegetation Fractional Cover (FVC) is defined as the ratio of vegetation shadow area to total area. This study utilized NDVI data from MOD13Q1 to calculate vegetation fractional cover using the dimidiate pixel model. The calculation formula is as follows [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eFVC=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{NDVI-{NDVI}_{S}}{{NDVI}_{V}-{NDVI}_{S}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eWhere FVC represents the fractional vegetation cover (FVC) of Kezhou; NDVI represents the Normalized Difference Vegetation Index of Kezhou; NDVI\u003csub\u003eS\u003c/sub\u003e represents the normalized vegetation index of pure bare soil pixels in Kezhou, where the minimum NDVI value is used in the calculation; and NDVI\u003csub\u003eV\u003c/sub\u003e represents the normalized vegetation index of pure vegetation pixels, where the maximum NDVI value is used in the calculation. Since the study area is located in an arid region, the FVC of the study area was classified into five levels based on the classification criteria for vegetation cover in arid areas proposed by Gao Jianjian et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The detailed classification is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFVC Type Classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFVC Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh cover\u003c/p\u003e \u003cp\u003eHigher cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u0026thinsp;\u0026le;\u0026thinsp;FVC\u0026thinsp;\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003cp\u003e0.5\u0026thinsp;\u0026le;\u0026thinsp;FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u0026thinsp;\u0026le;\u0026thinsp;FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026le;\u0026thinsp;FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;\u0026lt;\u0026thinsp;FVC\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Vegetation Net Primary Productivity (NPP)\u003c/h2\u003e \u003cp\u003eVegetation Net Primary Productivity (NPP) represents the total productivity of terrestrial plants that provides energy for autotrophs and other organisms, and can effectively assess vegetation health status and terrestrial carbon cycling in arid regions. Potter et al. proposed the CASA (Carnegie-Ames-Stanford Approach) model in 1993[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Scholars such as Jiao Wei employed the CASA model to combine remote sensing data, climate data, and plant physiological data, achieving dynamic spatiotemporal simulation of vegetation NPP [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In their research on carbon cycle balance of vegetation resources in the arid regions of Northwest China, Zhang Qifei, Chen Yaning, and others used the CASA model to estimate vegetation NPP in the study area, yielding relatively accurate results [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study adopts the CASA model methodology, considering factors such as the NDVI index, land change types, temperature (℃), and precipitation (mm) in the study area, and employs two variables\u0026mdash;APAR (Absorbed Photosynthetically Active Radiation) and ε (photosynthetic conversion efficiency)\u0026mdash;to estimate vegetation NPP. The expression is as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{P}\\text{P}(\\text{X},\\text{M})=\\text{A}\\text{P}\\text{A}\\text{R}(\\text{x},\\text{m})\\times\\:{\\epsilon\\:}(\\text{x},\\text{m})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere NPP(x, m) represents the net primary productivity of vegetation at pixel x in month m; APAR(x, m) represents the absorbed photosynthetically active radiation at pixel x in month m; and ε(x, m) represents the actual light use efficiency of vegetation at pixel x in month m.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Trend Analysis and Significance Testing\u003c/h2\u003e \u003cp\u003eThe Theil-Sen Median method, abbreviated as Sen trend analysis, offers high computational efficiency and is insensitive to errors in data series, making it commonly used for analyzing long-term time series data trends [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The Mann-Kendall significance test, abbreviated as M-K test, is a non-parametric time series significance trend test frequently employed to examine the significance of long-term variation trends [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. By combining the Sen and Mann-Kendall methods to test vegetation NPP change trends, noise interference can be effectively reduced, thereby improving the accuracy and significance of trend detection in time series vegetation NPP [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSen slope estimation calculates the median of pairwise comparisons within the study area over the study period to investigate the trend of vegetation NPP changes within a specific spatial and temporal context. The calculation formula is as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:MI=\\text{M}\\text{e}\\text{d}\\text{i}\\text{a}\\text{n}\\left(\\frac{{\\text{x}}_{\\text{j}}-{\\text{x}}_{\\text{i}}}{\\text{j}-\\text{i}}\\right)\\)\u003c/span\u003e \u003c/span\u003e,j\u0026le;2020, 2000\u0026thinsp;\u0026le;\u0026thinsp;i\u0026lt;2020 (3)\u003c/p\u003e \u003cp\u003e \u003cem\u003eMI\u003c/em\u003e represents the median slope of vegetation net primary productivity. The sign of MI (positive or negative) indicates the trend in the study series: MI\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates an increasing trend, while MI\u0026thinsp;\u0026lt;\u0026thinsp;0 indicates a decreasing trend. Median() is the median function, where \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e represent the values of the \u003cem\u003ei\u003c/em\u003eth and \u003cem\u003ejt\u003c/em\u003eh items in the time series, respectively. The magnitude of MI represents the average rate of change[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe M-K method is used to detect significant changes in vegetation NPP in the time series from 2000 to 2020. First, the paired data \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e are determined, which are random variables with no fixed increasing or decreasing trend. The test statistic S is shown in formula (4):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{S}={\\sum\\:}_{\\text{i}=1}^{\\text{n}-1}{\\sum\\:}_{\\text{j}=\\text{i}+1}^{\\text{n}}\\text{s}\\text{g}\\text{n}({\\text{x}}_{\\text{j}}-{\\text{x}}_{\\text{i}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e5\u003c/span\u003e), sgn represents the sign function:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{s}\\text{g}\\text{n}\\left({\\text{x}}_{\\text{j}-}{\\text{x}}_{\\text{i}}\\right)=\\left\\{\\begin{array}{c}+1,{\\text{x}}_{\\text{j}-}{\\text{x}}_{\\text{i}}\u0026gt;0\\\\\\:0,{\\text{x}}_{\\text{j}-}{\\text{x}}_{\\text{i}}=0\\\\\\:-1,{\\text{x}}_{\\text{j}-}{\\text{x}}_{\\text{i}}\u0026lt;0\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e6\u003c/span\u003e), Z is the standard normal distribution statistic:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\text{Z}=\\left\\{\\begin{array}{c}\\frac{\\text{S}-1}{\\sqrt{\\text{v}\\text{a}\\text{r}\\left(\\text{s}\\right)}},s\u0026gt;0\\\\\\:0,s=0\\\\\\:\\frac{\\text{S}+1}{\\sqrt{\\text{v}\\text{a}\\text{r}\\left(\\text{s}\\right)}},s\u0026lt;0\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e7\u003c/span\u003e), var(s) is the variance:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:\\text{v}\\text{a}\\text{r}\\left(\\text{s}\\right)=\\frac{\\text{n}(\\text{n}-1)(2\\text{n}+5)}{18}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Pearson Correlation Analysis\u003c/h2\u003e \u003cp\u003ePearson Correlation Analysis is used to examine the correlation relationship between two variables, i.e., predicting one variable based on another [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, the Pearson correlation method was employed to calculate the correlation between temperature, precipitation, and vegetation NPP on a pixel-by-pixel basis. Specifically, raster data of temperature and precipitation with the same pixel size as vegetation NPP were generated year by year in GIS software using the inverse distance weighting (IDW) interpolation method, and the correlation between temperature, precipitation, and NPP was calculated using Matlab software. The calculation formula is as follows:\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:\\:Re=\\frac{{\\sum\\:}_{i=1}^{n}(X-\\stackrel{-}{X})(Y-\\stackrel{-}{Y})}{\\sqrt[2]{{\\sum\\:}_{i=1}^{n}{X}^{2}\\sum\\:_{i=1}^{n}{Y}^{2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere\u003c/p\u003e \u003cp\u003e (9)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the value of the vegetation index (NDVI, NPP) in year \u003cem\u003ei\u003c/em\u003e;\u003cspan class=\"InlineEquation\"\u003e\u003c/span\u003erepresents the 20-year mean value of the vegetation index; \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the value of the correlating variable (temperature, precipitation) in year i; andrepresents the 20-year mean value of the correlating variable. Re ranges between [-1, 1]. According to the correlation classification criteria proposed by Chen Chunbo [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and other scholars, the classification adopted in this station adopted in this study is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRe judgement criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRe Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRe Classification Criterion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRe Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelation Trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⎢Re⎢\u0026thinsp;\u0026ge;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRe\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePositive correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026le;\u0026thinsp;⎢Re⎢\u0026lt;0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026le;\u0026thinsp;⎢Re⎢\u0026lt;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRe\u0026thinsp;\u0026lt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNegative correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⎢Re⎢\u0026lt;0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results and Analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatio-temporal Evolution Characteristics of Vegetation Index NDVI\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Temporal Evolution Characteristics and Trends of Vegetation NDVI\u003c/h2\u003e \u003cp\u003eFrom a temporal perspective, based on the spatio-temporal evolution characteristics of the vegetation growing season NDVI index in the study area from 2000 to 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the average NDVI index across the entire region fluctuated between 0.12 and 0.16 over the past 20 years, with an overall increasing rate of 0.0021. The 20-year mean NDVI value was 0.132. Specifically, the mean vegetation NDVI from 2000 to 2010 was 0.125, while the average NDVI from 2011 to 2020 was 0.141, representing a year-on-year increase of 15.3% (compared to the 2000\u0026ndash;2010 mean). During the period 2000\u0026ndash;2010, the annual mean vegetation NDVI in Kezhou showed positive year-on-year growth in three years (2002, 2006, and 2009), while the remaining years exhibited negative growth. During the period 2011\u0026ndash;2020, the annual mean vegetation NDVI showed positive growth in six years (2010, 2012, 2015, 2016, 2017, and 2020) and negative growth in three years. The annual mean vegetation NDVI during the latter decade was significantly higher than that of the previous decade (2000\u0026ndash;2010).\u003c/p\u003e \u003cp\u003eAs shown by the variation trend of the annual mean NDVI in Kezhou over the 20-year period (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the annual mean NDVI in the Kezhou fluctuated within the range of 0.16\u0026ndash;0.22, exhibiting an overall slow upward trend. During the period 2000\u0026ndash;2010, the annual mean NDVI increased from approximately 0.16 to 0.19, representing an increase of approximately 18.8%; during the period 2011\u0026ndash;2020, the annual mean NDVI increased from approximately 0.19 to 0.21, representing an increase of approximately 10.5%, indicating a deceleration in the growth rate during the latter period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Spatial Distribution Characteristics of Vegetation NDVI in Kezhou\u003c/h2\u003e \u003cp\u003eKezhou's average NDVI spatial distribution (2000\u0026ndash;2020) exhibits significant differentiation: high in the northwest, low in the southeast, with strip patterns along water systems and oases. High NDVI areas (0.312\u0026ndash;0.739) concentrate in the piedmont oasis belt at the southern Tianshan Mountains, northeastern river valleys, and intermountain basins. Relying on stable snow/ice meltwater and rivers ( Kezhou, Gaizi), these form the main agricultural zones and natural oases with NDVI above 0.458, some exceeding 0.7, demonstrating a distinct \"oasis effect.\" Medium NDVI regions (0.131\u0026ndash;0.312) occur at oasis peripheries, river terraces, and low mountain hilly areas as ecological transition zones dominated by grasslands, shrubs, and dryland farming with patchy distributions. Low NDVI areas (0.008\u0026ndash;0.131) cover southern and central regions including the Taklamakan Desert, Gobi, and bare rock, with sparse desert vegetation generally below 0.131. Water resources constitute the core determinant of NDVI patterns, while topography regulates distribution through precipitation and meltwater convergence. Human activities significantly influence spatial distribution\u0026mdash;agricultural irrigation areas display higher NDVI than surrounding natural deserts, reflecting artificial oasis enhancement, whereas scattered desert-edge oases show lower coverage due to limited water. Overall, NDVI distribution reflects both arid region ecosystem vulnerability and the regulatory roles of water resources and human activities.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Spatiotemporal Evolution Characteristics of Fractional Vegetation Cover (FVC) in Kezhou\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the spatial distribution patterns and dynamic change characteristics of Fractional Vegetation Cover (FVC) in the study area for four periods: 2000, 2005, 2015, and 2020.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study area shows a spatial pattern of \"high in the northwest and southeast, low in the southwest,\" with high vegetation coverage concentrated in northwestern mountainous regions and low coverage in southeastern plains and urban agglomerations. From 2000 to 2020, vegetation coverage exhibited an overall improving trend, characterized by continuous expansion of high and sub-high FVC areas and gradual reduction of low and sub-low FVC areas. Improvement was modest during 2000\u0026ndash;2005 with minimal spatial pattern changes, while 2005\u0026ndash;2015 witnessed the most significant improvement, with green areas expanding southeastward, indicating positive effects from ecological restoration projects or climatic factors.\u003c/p\u003e \u003cp\u003eThe bar chart reveals significant structural changes in vegetation coverage from 2001 to 2020, showing an overall optimization trend of \"decreasing low coverage grades and increasing medium-high coverage grades.\" Relatively low vegetation coverage remained dominant, fluctuating and declining from approximately 82% in 2001 to 74% in 2005, rebounding to 80% in 2010, slightly decreasing to 79% in 2015, then substantially increasing to 91% in 2020. Low vegetation coverage showed a consistent downward trend from 7% to 2%, reflecting significant ecological improvement. Moderate vegetation coverage peaked at 13% in 2005, fluctuating between 8%\u0026ndash;11% in other years. Both relatively high and high vegetation coverage exhibited slow upward trends, increasing from 2% to 6% and 1% to 3% respectively, indicating gradual expansion of high-grade vegetation areas.\u003c/p\u003e \u003cp\u003eIn summary, vegetation coverage in the region transformed from \"medium-low coverage dominance with relatively high low coverage proportion\" to \"absolute dominance of medium-low coverage with gradual high coverage development,\" demonstrating stable and positive ecological environmental quality improvement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spatio-temporal Evolution Characteristics of Vegetation NPP in Kezhou\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Temporal Distribution Characteristics of Vegetation NPP in Kezhou\u003c/h2\u003e \u003cp\u003eThis line chart illustrates the inter-annual variation trend of vegetation Net Primary Productivity (NPP) in the Kezhou from 2000 to 2020. The figure clearly reveals that over the past 21 years, vegetation NPP in this region has undergone a three-stage evolution process of \"fluctuating increase\u0026mdash;sharp decline\u0026mdash;low-level fluctuation,\" exhibiting an overall inverted \"V\"-shaped pattern characterized by initial growth followed by decline.\u003c/p\u003e \u003cp\u003eSpecifically, during the period 2000\u0026ndash;2015, vegetation NPP demonstrated an overall fluctuating upward trend, gradually increasing from approximately 42 g C/m\u0026sup2; in 2000. This period witnessed multiple minor fluctuations, including brief declines during 2003\u0026ndash;2004, adjustments during 2007\u0026ndash;2008, and further fluctuations during 2011\u0026ndash;2012. Nevertheless, the overall upward trend remained evident, reaching a peak of approximately 51 g C/m\u0026sup2; in 2015. The average annual growth rate during this stage was approximately 0.06 g C/m\u0026sup2;, reflecting continuous improvement in regional vegetation productivity, which may be closely associated with the implementation of ecological restoration projects, climate warming and humidification, and other related factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Spatial Evolution Trends and Significance Testing of Vegetation NPP in Kezhou\u003c/h2\u003e \u003cp\u003eNPP spatial distribution (2000\u0026ndash;2020) exhibits significant heterogeneity characterized by \"high in the north and low in the south, high in the east and low in the west, high in mountainous areas and low in plains.\" High NPP areas (green) concentrate in northern and northeastern mountainous regions in strip distributions, reaching above 57.83 g C/m\u0026sup2; with alpine meadows and forests showing strong photosynthetic capacity. Two distinct high NPP zones around water bodies appear in the east-central part, displaying \"water body-oasis\" synergistic characteristics of irrigated agriculture or riparian vegetation. Medium-high NPP areas (yellow-green) appear patchily in northwestern mountains, while vast southwest and southeast plains are dominated by low NPP areas (dark red) approaching 0 g C/m\u0026sup2;, indicating sparse vegetation constrained by extreme aridity, poor soil, or intensive human activities. NPP grades present gradient succession from north to south and mountains to plains, with topography playing a decisive role. Low NPP areas occupy absolute advantage in total area while high NPP areas show island-like distribution, indicating relatively low overall productivity and fragile ecological baseline conditions. Despite inter-annual fluctuations, the spatial pattern remained stable, coupled with topographic, climatic, and hydrological differentiation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignificance testing reveals insignificant increase (yellow-green) dominates approximately 85\u0026ndash;90%, extensively distributed across south-central plains, eastern mountains, and western areas, indicating stable or slowly improving ecosystems. Significant increase areas (dark red, ~\u0026thinsp;5\u0026ndash;8%) concentrate in northwestern and central-northern mountainous zones with statistically reliable improvement. Insignificant decrease areas (blue, ~\u0026thinsp;4\u0026ndash;6%) embed within northeastern high NPP baseline areas, while significant decrease (white) is extremely rare (~\u0026thinsp;0\u0026ndash;1%). Notably, significant increase areas mainly locate in medium NPP baseline areas whereas high NPP baseline areas show insignificant trends, implying ecological restoration achieved more significant results in moderately conditioned areas. Overall, vegetation NPP changes were primarily characterized by insignificant increase with significant improvement concentrated in northwestern and northern mountainous regions, demonstrating a stable and positive development trend.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRe judgement criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification Standard\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea Percentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsignificant decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026thinsp;\u0026le;\u0026thinsp;0,Z\u0026thinsp;\u0026ge;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant decrease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026thinsp;\u0026lt;\u0026thinsp;0, Z\u0026thinsp;\u0026lt;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsignificant increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026thinsp;\u0026ge;\u0026thinsp;0,Z\u0026thinsp;\u0026lt;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026thinsp;\u0026gt;\u0026thinsp;0, Z\u0026thinsp;\u0026gt;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Analysis of Climate Change Drivers of Vegetation NPP","content":"\u003cp\u003eThis study employed Pearson correlation analysis between annual mean climate factors (temperature and precipitation, 2000\u0026ndash;2020) and 20-year mean NPP via pixel-by-pixel calculations. Temperature-NPP correlation ranged from \u0026minus;\u0026thinsp;0.792 to 0.875, with positive correlations covering 43.7% of the area, mainly in high population density zones and southwestern plateau regions (blue areas in precipitation figure). This indicates human interventions (irrigated agriculture) or growing season extension in high-altitude cold environments promoted productivity. Negative temperature correlations occupied 23% (large red regions in temperature figure), where warming intensified evapotranspiration in central arid watersheds, causing vegetation water stress and decreased productivity due to insufficient precipitation compensation.\u003c/p\u003e \u003cp\u003ePrecipitation-NPP correlation ranged from \u0026minus;\u0026thinsp;0.81 to 0.86, with positive correlations covering 50.3% (exceeding temperature) and negative correlations at 19.2%. Central arid regions exhibited \"precipitation-positive dominance with temperature-negative correlation,\" indicating water availability as the core factor controlling vegetation productivity, while warming negatively affected productivity through enhanced evapotranspiration. The southwestern plateau showed positive correlations with both factors (blue in both figures), where temperature increases promoted alpine snow/ice melt, increasing river runoff and irrigation water, forming a \"warm-humid\" synergistic promotion contrasting with the \"warm-dry\" stress in central watersheds.\u003c/p\u003e \u003cp\u003eOverall, vegetation NPP response to climate factors exhibits significant spatial heterogeneity, with water availability as the key explanatory variable. The combined effects of water-heat conditions, topography, and human activities shaped this complex spatial pattern.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Analysis of Spatiotemporal Evolution Characteristics of Vegetation Index NDVI\u003c/h2\u003e \u003cp\u003eKezhou's NDVI increased at 0.0021/year from 2000\u0026ndash;2020, showing slow improvement consistent with vegetation growth trends in Northwest China but below the regional average, reflecting restoration difficulties in extreme arid zones. Temporally, mean NDVI during 2011\u0026ndash;2020 (0.141) increased 15.3% compared to 2000\u0026ndash;2010 (0.125), with positive growth years rising from 3 to 6, indicating significantly improved vegetation trends in the recent decade, likely related to continued implementation of ecological projects like \"Grain for Green.\"\u003c/p\u003e \u003cp\u003eSpatially, NDVI presents a \"high northwest, low southeast\" pattern distributed along water systems and oases, consistent with research on Xinjiang's ecological vulnerability. High-value areas concentrate in the Tianshan Mountains' southern piedmont oasis belt and river valleys, exceeding 0.458 and demonstrating the \"oasis effect,\" while southern Taklamakan Desert edges remain below 0.131. This differentiation is determined by hydrothermal conditions: northwestern mountains receive abundant precipitation from orographic lifting plus snow/ice meltwater, whereas southeastern plains suffer severe water stress from scarce precipitation and intense evaporation.\u003c/p\u003e \u003cp\u003eNotably, Kezhou's NDVI growth rate (0.18 \u0026times; 10⁻\u0026sup2;/year) is significantly lower than the Yellow River basin and Aksu region, closely related to its \"three mountains sandwiching two valleys\" topography. Mountains exceed 90% of the area with limited usable oasis and fragile ecosystems, resulting in slow vegetation restoration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Evolution Characteristics and Ecological Significance of Fractional Vegetation Cover (FVC)\u003c/h2\u003e \u003cp\u003eFVC classification results reveal that medium-coverage vegetation expansion was most significant from 2000\u0026ndash;2020, increasing from 10.9% to 17.93% (64.4% growth), becoming the main contributor to vegetation cover structure optimization\u0026mdash;similar to Loess Plateau research findings, indicating that arid area restoration first manifests as medium-coverage region expansion. Low-coverage areas decreased from 7% to 2%, demonstrating significantly improved ecological conditions and alleviated desertification.\u003c/p\u003e \u003cp\u003eHowever, high-coverage and relatively high-coverage proportions remain low (~\u0026thinsp;9% combined in 2020) with slow growth, indicating vegetation quality remains at medium-to-low levels. In 2020, medium-low coverage accounted for 91%, showing the vegetation body remains sparse vegetation and grasslands with simple ecosystem structure and insufficient stability. Future restoration should focus on improving cover quality and promoting conversion from low to medium-high coverage.\u003c/p\u003e \u003cp\u003eSpatially, 2005\u0026ndash;2015 witnessed the fastest FVC improvement with southeastward green expansion, consistent with rising NPP trends and reflecting superimposed effects of ecological projects and climate warming/humidification. After 2015, improvement slowed and stabilized, possibly related to ecosystem equilibrium or intensified water resource constraints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Spatio-temporal Evolution of Vegetation NPP and Climate Driving Mechanisms\u003c/h2\u003e \u003cp\u003eKezhou's vegetation NPP exhibited a three-stage evolution: \"fluctuating increase\u0026mdash;sharp decline\u0026mdash;low-level fluctuation.\" After peaking in 2015 (~\u0026thinsp;5.1 g C/m\u0026sup2;), it experienced a cliff-like decline, maintaining low levels of 3.8\u0026ndash;3.9 g C/m\u0026sup2; during 2017\u0026ndash;2020. This contrasts with continuous FVC improvement, revealing asynchrony between vegetation coverage (quantity) and productivity (quality). The sharp NPP decline may be attributed to:\u003c/p\u003e \u003cp\u003e(1) Climatic anomalies: Periodic drought after 2015 featured decreasing precipitation and rising temperatures, intensifying evapotranspiration and vegetation water stress. Temperature shows significant negative correlation with NPP in central arid watersheds, differing from conclusions that temperature has minimal effects on Xinjiang grassland NPP.\u003c/p\u003e \u003cp\u003e(2) Water-heat combination effects: The southwestern plateau shows positive correlation with both factors (warming promotes snow/ice melt, increasing runoff), forming a \"warm-humid\" synergistic effect. Conversely, central watersheds exhibit \"warm-dry\" stress, with precipitation-positive areas (50.3%) exceeding temperature-positive areas (43.7%), indicating water availability as the core productivity control factor.\u003c/p\u003e \u003cp\u003e(3) Ecosystem threshold effects: Continuous increase before 2015 may have approached or exceeded local water resource carrying capacity thresholds, causing subsequent productivity decline\u0026mdash;suggesting ecological thresholds exist for arid region restoration.\u003c/p\u003e \u003cp\u003e(4) Significance testing shows 88% of the region exhibited insignificant NPP increases, with only 6.5% significant increases located in medium NPP baseline areas rather than high baseline areas. This \"middle breakthrough\" effect implies ecological projects achieve better results in moderately conditioned areas, providing reference for differentiated restoration strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Spatial Heterogeneity of Climate Factor Driving Mechanisms\u003c/h2\u003e \u003cp\u003ePearson correlation analysis reveals significant spatial heterogeneity in Kezhou's vegetation NPP response to climate factors. (1) Dual temperature effects: Positive correlations (43.7%) occur in densely populated areas (irrigated agriculture heat utilization) and the southwestern plateau (growing season extension in high-altitude cold environments). Negative correlations (23%) concentrate in central arid watersheds where warming intensifies evapotranspiration and insufficient precipitation causes water stress. (2) Precipitation dominance: Positive correlation areas (50.3%) exceed temperature, mainly in the southwestern plateau and major river basins, indicating moisture as the primary growth-limiting factor\u0026mdash;consistent with Chen Chunbo et al.'s Xinjiang grassland conclusions. Negative correlation areas (19.2%) occur in the Tarim Basin central desert and Gobi, where increased precipitation may accompany extreme weather or fail to convert to available moisture, potentially triggering runoff erosion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Research Limitations and Prospects\u003c/h2\u003e \u003cp\u003eThis study has limitations: (1) CASA model estimation didn't fully consider soil types, CO₂ effects, and human management practices, potentially biasing agricultural estimates; (2) uneven meteorological station distribution, particularly sparse coverage in northwestern mountains, introduces interpolation uncertainty; (3) only temperature and precipitation were analyzed, excluding solar radiation, wind speed, and human activities.\u003c/p\u003e \u003cp\u003eFuture research should: (1) integrate multi-source remote sensing (Landsat, Sentinel-2) to improve spatial resolution; (2) employ Geodetector and multiple regression to quantitatively distinguish climate and human contributions; (3) couple NPP with ecosystem service values and carbon sequestration to support \"dual carbon\" goals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Implications for Sustainable Development\u003c/h2\u003e \u003cp\u003eThis study provides critical insights for achieving multiple Sustainable Development Goals (SDGs) in arid regions, particularly in the context of climate change and ecosystem restoration.\u003c/p\u003e \u003cp\u003eSDG 13 (Climate Action): Our findings reveal the asymmetric response of vegetation productivity to climate warming in Kezhou, with central watersheds experiencing \"warm-dry\" stress while the southwestern plateau benefits from \"warm-humid\" conditions. This spatial heterogeneity highlights the need for climate adaptation strategies that account for local geomorphological gradients rather than uniform regional policies. The identification of ecosystem threshold effects\u0026mdash;where NPP collapsed after 2015 despite continued greening\u0026mdash;demonstrates the vulnerability of arid ecosystems to climatic extremes, underscoring the urgency of climate action in these fragile environments. The precipitation-dominant control (50.3% positive correlation) over vegetation productivity emphasizes that water resource management is central to climate adaptation in arid regions.\u003c/p\u003e \u003cp\u003eSDG 15 (Life on Land): The dominance of medium-low vegetation coverage (91% in 2020) and the low proportion of high-coverage areas (3%) indicate that Kezhou's ecosystem remains fragile with simple vegetation structure. Our recommendation for \"water-limited greening\" and differentiated restoration strategies for medium-coverage areas directly supports the UNCCD's Land Degradation Neutrality targets. The expansion of medium FVC (64.4% growth from 10.9% to 17.93%) demonstrates progress in combating desertification, but the stagnation of high-coverage vegetation and the sharp NPP decline after 2015 suggest that achieving SDG 15's ambition of \"protecting, restoring and promoting sustainable use of terrestrial ecosystems\" requires long-term commitment and quality-oriented rather than merely coverage-oriented restoration approaches.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eBased on MODIS and meteorological data (2000\u0026ndash;2020), this study employed Theil-Sen, Mann-Kendall, and Pearson correlation analyses to reveal spatio-temporal vegetation evolution and climate drivers in Kezhou. Key findings: (1) NDVI increased at 0.0021/year, with improvement accelerating post-2011. Spatially, it shows \"high northwest, low southeast\" patterns along water systems, with high values (\u0026gt;\u0026thinsp;0.458) in Tianshan piedmont oases and low values (\u0026lt;\u0026thinsp;0.131) in southern deserts. (2) FVC structure optimized: medium coverage expanded from 10.9% to 17.93%, low coverage decreased from 7% to 2%, though high coverage remains low (3%) and medium-low coverage dominates at 91%. (3) NPP peaked at 5.1 g C/m\u0026sup2; in 2015 then declined 25.5% to 3.8 g C/m\u0026sup2;, showing \"rise then fall\" trends with 88% of areas exhibiting insignificant increases. (4) Climate responses show spatial heterogeneity: precipitation-positive areas (50.3%) exceed temperature (43.7%), with central watersheds showing \"warm-dry\" stress and southwestern plateau showing \"warm-humid\" synergy. (5) The ecosystem is improving but vulnerable, particularly to warming-drying trends. Recommendations include water-limited greening, differentiated strategies for medium-coverage areas, and cross-border water cooperation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Wang Jianping wrote the original manuscript; Aynur Mamat and Aida Orozobekova designed the experimental method; Almazbek Arzybaev revised the paper. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo Fundings .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eWe would like to thank the anonymous reviewers for their constructive com-\u003c/p\u003e\n\u003cp\u003ements that helped to improve the quality of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGaur S, Mittal A, Bandyopadhyay A, et al. 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Theoret Appl Climatol. 2018;131(3/4):1503\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Net Primary Productivity (NPP), Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), climate factors","lastPublishedDoi":"10.21203/rs.3.rs-9094169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9094169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the spatio-temporal evolution of vegetation ecosystems and climate-NPP relationships in Kezhou, Northwest China, from 2000 to 2020. Using preprocessed MOD13Q1 data, we analyzed NDVI, FVC, and NPP trends. Theil-Sen Median trend analysis combined with Mann-Kendall test was employed to examine NPP variation, while Pearson correlation analysis was applied pixel-by-pixel to explore temperature-precipitation-NPP relationships.\u003c/p\u003e \u003cp\u003eResults show: (1) NDVI increased at a rate of 0.0021/year, with the mean value in 2011\u0026ndash;2020 (0.141) exceeding that of 2000\u0026ndash;2010 (0.125), displaying a \"high northwest, low southeast\" spatial pattern; (2) medium FVC (0.3\u0026ndash;0.5) expanded significantly from 10.9% to 17.93%, although medium-low coverage still dominated at 91% in 2020; (3) NPP fluctuated upward initially, rising from 42 g C/m\u0026sup2; (2000) to a peak of 51 g C/m\u0026sup2; (2015), then declined sharply to 3.8 g C/m\u0026sup2; in 2016\u0026ndash;2017 (\u0026ndash;25.5%) and maintained low levels thereafter, with spatial distribution showing \"high north, low south\" and 88% of the area exhibiting insignificant trends; (4) precipitation showed positive correlation with NPP across 50.3% of the area, exceeding temperature (43.7%), with central arid basins demonstrating \"precipitation-positive, temperature-negative\" patterns while the southwestern plateau exhibited warm-humid synergy.\u003c/p\u003e \u003cp\u003eThese findings indicate that while vegetation coverage achieved continuous improvement, post-2015 NPP dynamics reveal critical insights for sustainable ecosystem management: water availability emerges as the dominant controlling factor for productivity, and the identified climate-vegetation relationships provide a scientific foundation for adaptive strategies to address warming-drying challenges in central basins, thereby supporting targeted ecological restoration and enhanced climate resilience in arid regions. This research contributes to SDG 13 (Climate Action) and SDG 15 (Life on Land) by offering practical guidance for ecosystem restoration and sustainable land management in Belt and Road node areas.\u003c/p\u003e","manuscriptTitle":"Spatio-temporal Evolution of Vegetation and Its Climatic Driving Factors in the Kezhou, Northwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 11:52:23","doi":"10.21203/rs.3.rs-9094169/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T01:41:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115946002480608750333803954582347476649","date":"2026-05-14T11:38:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241415528738886128566429208088407752658","date":"2026-05-14T01:55:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236459807700578156211923749971583619503","date":"2026-05-14T01:55:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338038225579334304720812879679333860818","date":"2026-05-11T06:36:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35402448527872635862765860322469649907","date":"2026-05-07T12:35:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T14:50:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T07:12:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T11:24:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-04-08T10:44:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2b6609d-24d0-4e0a-b84c-f9280dba3563","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T01:41:33+00:00","index":73,"fulltext":""},{"type":"reviewerAgreed","content":"115946002480608750333803954582347476649","date":"2026-05-14T11:38:28+00:00","index":72,"fulltext":""},{"type":"reviewerAgreed","content":"241415528738886128566429208088407752658","date":"2026-05-14T01:55:39+00:00","index":71,"fulltext":""},{"type":"reviewerAgreed","content":"236459807700578156211923749971583619503","date":"2026-05-14T01:55:17+00:00","index":70,"fulltext":""},{"type":"reviewerAgreed","content":"338038225579334304720812879679333860818","date":"2026-05-11T06:36:46+00:00","index":57,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:39:27+00:00","index":39,"fulltext":""},{"type":"reviewerAgreed","content":"35402448527872635862765860322469649907","date":"2026-05-07T12:35:28+00:00","index":38,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T11:52:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 11:52:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9094169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9094169","identity":"rs-9094169","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}