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Frontiers in Environmental Science ·

Volume 14 - 2026 | https://doi.org/10.3389/fenvs.2026.1982914

Effects of climate change and human activities on vegetation net primary productivity: a case study of Henan Province, China

  • Zheng Yuan1
  • Jingge Lu2
  • Zhigao Zhang2*corresponding author
  • Jingyu An2
  • Ruijiao Zhang2
  • Huayong Li2
  • Linsong Yu2
  • Maotang Cai3*corresponding author
  1. 1School of Mathematical and Information Science, Anyang Institute of Technology, Anyang, Henan, China
  2. 2School of Resources Environment and Tourism, Anyang Normal University, Anyang, Henan, China
  3. 3Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China
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Abstract

Net primary productivity (NPP) is a key indicator of the carbon-sink capacity of terrestrial ecosystems. Annual MODIS NPP data from 2001 to 2024 were used to characterize the spatiotemporal dynamics, stability, and future trajectories of NPP in Henan Province using Theil-Sen trend analysis, the Mann-Kendall test, the coefficient of variation, the Hurst exponent, and the gravity-center model. Partial derivative analysis was then applied to quantify the relative contributions of climatic factors (temperature, precipitation, and solar radiation) and human activities to observed NPP changes. Results indicate that, over the 24-year period, NPP increased significantly at a rate of 4.43 g C·m -2 ·a −1 (P < 0.01), with a multiyear mean of 486.88 g C·m -2 ·a −1. NPP varied substantially among land-use types, with the highest values in woodland, followed by grassland and farmland, and the lowest values in barren land. Spatially, high NPP values occurred in western and southern Henan, whereas low values occurred in eastern and northern Henan. NPP increased across 94.70% of the vegetated area, with significant increases (64.41%) concentrated in the central-western and southern regions; areas of decline (3.14%) were mainly distributed along the Beijing-Guangzhou and Lianyungang-Lanzhou railways corridors. NPP was generally stable over time (mean CV = 0.14); however, a mean Hurst exponent of 0.46 suggested that 69.07% of the region would exhibit anti-persistence characteristics. The NPP gravity center shifted 4.58 km northwestward during the study period. Climate change and human activities contributed 43.19% and 56.81% to NPP changes, respectively. Human activities were the primary driver of vegetation improvement (57.85%), whereas climate change dominated vegetation degradation (67.82%). Although ecological restoration and agricultural modernization have partly offset the adverse effects of climate change, a substantial risk of future degradation remains. Targeted adaptive management is needed in high-risk areas to safeguard regional carbon sinks and sustain ecosystem services.

Keywords

1 Introduction

Global climate change and intensifying human activities are reshaping terrestrial ecosystems (Walther et al., 2002; Konapala et al., 2020). Vegetation mediates critical interactions among the atmosphere, hydrosphere, pedosphere, and biota, and plays an essential role in regulating the global carbon cycle, maintaining climatic stability, and supporting human wellbeing (Frank et al., 2015; Yao et al., 2020). Net primary productivity (NPP), defined as the net carbon assimilated by green plants per unit area and time after autotrophic respiration is accounted for, directly measures vegetation carbon-fixation capacity (Piao et al., 2005; Xue et al., 2023). Consequently, NPP serves as both a key indicator of ecosystem productivity and an integrative variable linking ecosystem structure, functional integrity, carbon-sink potential, and ecological sustainability (Wu et al., 2020; Xu et al., 2020). Understanding its spatiotemporal evolution and underlying drivers is therefore essential for assessing ecosystem health and developing science-based ecological management policies under ongoing climate change (Duveneck and Thompson, 2017; Song et al., 2021).

NPP was traditionally estimated from ground-based plot measurements, which provide reliable information at local scales but are limited for large-area, long-term monitoring (Chen et al., 2023; Yang et al., 2024). The development of remote-sensing technologies has enabled large-scale NPP estimation through a range of modeling frameworks, including statistical, process-based, and light-use-efficiency models (Lieth, 1975; Running et al., 2004; Hasenauer et al., 2012; Wang F. et al., 2020; Chen et al., 2025). Building on these approaches, numerous studies have examined spatiotemporal patterns of NPP and their drivers across diverse ecosystems (Machado-Silva et al., 2021; Wei et al., 2022; Wang S. M. et al., 2025). At the global scale, long-term NPP has increased significantly, particularly in the mid-latitudes of the Northern Hemisphere (Tucker et al., 2001). Similar greening trends have been observed across China, although marked regional disparities remain (Zhu et al., 2023; Xie et al., 2025; Meng et al., 2026b; Zhang Z. H. et al., 2026).

Climatic change and human activities are widely recognized as primary drivers of NPP dynamics (Zhu et al., 2016; Meng et al., 2026a). Climatic factors, particularly temperature and precipitation, regulate NPP by influencing growing-season length, photosynthetic rates, and water-use efficiency (Feldman et al., 2024). At the same time, rapid urbanization and economic development have increasingly influenced vegetation dynamics (Chen and Zhang, 2023). Large-scale ecological initiatives, such as afforestation and the Grain for Green program, have facilitated vegetation recovery (Cai et al., 2020; Wang F. et al., 2025), whereas urban expansion, overgrazing, and intensive land development have exacerbated land degradation and vegetation loss in some regions (Wang et al., 2022; Zhang F. G. et al., 2024).

Previous studies have applied linear correlation (Liu et al., 2018), multiple correlation (Yang et al., 2023), multiple regression (Xin et al., 2008), the Geodetector model (Zhi et al., 2024; Kumar et al., 2025), and residual analysis (He et al., 2025) to identify NPP drivers. However, these approaches are primarily correlative or qualitative and do not readily quantify the independent contribution of individual drivers. Partial derivative analysis provides a useful alternative because it isolates the partial effect of each climatic or human factor on vegetation change while controlling for the others. This approach has been applied in recent hydrological and ecological attribution studies (Qu et al., 2020).

Henan Province, in central-eastern China, spans the transitional zone between the second and third topographic steps and has a continental monsoon climate that transitions from northern subtropical to warm temperate conditions. As a major grain-producing area, a densely populated province, and an important ecological barrier, Henan is strategically important for national food security and ecological stability (Li et al., 2026). In recent decades, vegetation has changed substantially under the combined pressures of climate change and rapid urbanization. Although NPP changes in Henan have received increasing attention, most existing studies have relied on conventional statistical approaches that cannot effectively distinguish the contributions of different drivers (Cao et al., 2024). Therefore, MODIS NPP data from 2001 to 2024 obtained through the Google Earth Engine (GEE) platform were used with a framework incorporating trend analysis, the coefficient of variation, the Hurst exponent, and gravity-center modeling to assess NPP dynamics, stability, and future trends in Henan. Partial derivative analysis was further used to quantify the contributions of temperature, precipitation, solar radiation, and human activities to NPP changes. Specifically, this study aimed to (1) characterize the spatiotemporal evolution of NPP in Henan during 2001–2024; (2) evaluate its temporal stability and project future change directions; and (3) distinguish the relative contributions of climate change and human activities to NPP dynamics, thereby providing a scientific basis for regional ecosystem management and sustainable development.

2 Materials and methods

2.1 Study area

Henan Province (110°21′–116°39′ E, 31°23′–36°22′ N) is located in central China along the middle and lower reaches of the Yellow River. The province covers approximately 16.7 × 104 km2 and spans the transitional zone between China’s second and third topographic steps (Figure 1). The terrain slopes downward from west to east. The Taihang Mountains in the north, the Funiu Mountains in the west, and the Tongbai-Dabie Mountains in the south form a semicircular boundary. The central-eastern part consists of the Huang-Huai-Hai alluvial plain, whereas the southwestern part comprises the Nanyang Basin. Plains, mountains, and hills account for 55.7%, 26.6%, and 17.7% of the provincial area, respectively. Farmland covers 762.52 × 104 hm2, or approximately 45.66% of the province, confirming Henan’s role as an important nationally grain-production base. The region has a continental monsoon climate, transitioning from northern subtropical to warm temperate conditions, with heat and rainfall occurring in the same season. Mean annual temperature ranges from 12 °C to 16 °C, mean annual precipitation from 600 to 1,200 mm, annual sunshine duration from 1,740 to 2,310 h, and the frost-free period from 190 to 230 days. The eastern plains are dominated by farmland, whereas the western mountainous and hilly regions retain natural vegetation, including forests and shrublands.

Geographic location, elevation, and land-use types of Henan Province, China. **(a)** Location of the study area in China; **(b)** elevation distribution and city boundaries within Henan Province; **(c)** land-use types distribution.
Figure 1. Geographic location, elevation, and land-use types of Henan Province, China. (a) Location of the study area in China; (b) elevation distribution and city boundaries within Henan Province; (c) land-use types distribution.

2.2 Data sources and preprocessing

Three categories of data were compiled: vegetation NPP, climatic variables, and land-use/land-cover data. NPP data were obtained from the MOD17A3HGF.006 product (500 m native pixel size) provided by NASA’s EOS/MODIS platform. The NPP band was reported in kg C·m−2 with a scale factor of 0.0001. This scale factor was applied, and annual values were converted to g C·m-2·a−1 by multiplication by 1, 000; this unit was used throughout the manuscript. Quality filtering and annual compositing were performed on the GEE platform (https://lpdaac.usgs.gov), followed by masking and spatial alignment in ArcGIS.

Climatic data for temperature, precipitation, and solar radiation were derived from the ERA5-Land monthly reanalysis dataset provided by ECMWF (https://www.ecmwf.int/en/era5-land). The climatic variables were resampled to the 500 m NPP grid using cubic convolution for spatial alignment. The 500 m resolution was therefore treated as the common analysis grid rather than as the intrinsic spatial resolution of the ERA5-Land climatic data.

Land-use/land-cover data were obtained from the MCD12Q1 product provided by NASA’s EOS/MODIS platform. Five temporal snapshots (2001, 2006, 2012, 2018, and 2024) were extracted at a 500 m resolution. To minimize the confounding effects of land-use change on NPP trend analysis, stable land-use types were identified using a multitemporal frequency-based approach. The original land-use types were reclassified into six categories: farmland, woodland, grassland, built-up land, water bodies, and barren land. Digital elevation model (DEM) data were obtained from the Geospatial Data Cloud platform of China at a scale of 1:50,000 and a resolution of 30 m.

2.3 Methods

2.3.1 Trend analysis

The Theil-Sen slope estimator is a nonparametric method for estimating monotonic trends. Because it is relatively insensitive to measurement errors and outliers, it is suitable for long-term time-series analysis (Jiang H. et al., 2020). The formula is shown in Equation 1:where x *i* and x *j* are the NPP values in years i and j, respectively. A positive β indicates an increasing trend, whereas a negative β indicates a decreasing trend.

β = Median x j ‐ x i j ‐ i 0 < i < j < n (1)

The Mann–Kendall trend test is a nonparametric statistical method that is unaffected by missing data and outliers and is widely used to assess the statistical significance of trends in long-term hydrological, meteorological, and ecological time series (Mann, 1945). We adopted a significance level of α = 0.05. By combining the Theil–Sen slope β with the Mann–Kendall statistic Z, trends were classified as follows: significant increase (β > 0, |Z| > 1.96), no significant increase (β > 0,|Z| ≤ 1.96), significant decrease (β < 0, |Z| > 1.96), no significant decrease (β < 0,|Z| ≤ 1.96), and unchanged (β = 0).

2.3.2 Stability analysis

The coefficient of variation (CV) is widely used to assess the stability of time-series data (Wang et al., 2023). In this study, CV was used to characterize NPP stability in Henan Province from 2001 to 2024 and was calculated using Equation 2:where CV is the coefficient of variation of NPP, σ is the standard deviation of NPP, and x ¯ is the mean NPP over the study period. A larger CV indicates greater dispersion and stronger temporal fluctuation, whereas a smaller CV indicates greater stability. The CV values were classified into five levels: very stable (CV ≤ 0.05), moderately stable (0.05 < CV ≤ 0.10), medium stable (0.10 < CV ≤ 0.15), moderately unstable (0.15 < CV ≤ 0.20), and very unstable (CV > 0.20).

C V = σ x ¯ (2)

2.3.3 Hurst index

The Hurst exponent was estimated using rescaled-range (R/S) analysis to quantify the persistence of a time series (Wang B. et al., 2020). For an NPP time series {NPP(*t*), t = 1, 2, … , n}, the calculation steps are shown in Equations 3, 4: where NPP(*t*) is the NPP value at time t; NPP τ ¯ is the mean value of NPP(*t*); X (t, τ) is the cumulative deviation; t is time; n is the series length; R (*τ*) is the range; and S (*τ*) is the standard deviation at scale τ. If R (τ/S (τ ) α c τ H, the time series exhibits the Hurst phenomenon. The Hurst exponent H is estimated by least-squares fitting of lg R τ / S τ = lg ⁡ σ + H l g τ. H = 0.5 indicates random variation; H > 0.5 indicates positive persistence; and H < 0.5 indicates negative persistence.

R τ = X t , τ − 1 ≤ t ≤ τ max X t , τ 1 ≤ t ≤ τ min τ = 1 , 2 , … n (3)

S τ = 1 τ ∑ t = 1 τ N P P t − N P P τ ¯ 2 1 2 τ = 1 , 2 , … n (4)

By combining Theil-Sen trend analysis with the Hurst exponent, future NPP trends were classified into five types: continuous degradation (β < 0, H > 0.5), future degradation (β > 0, H < 0.5), future improvement (β < 0, H < 0.5), continuous improvement (β > 0, H > 0.5), and unpredictable change (H = 0.5).

2.3.4 Gravity center model

We used the gravity-center model to track the spatial centroid of NPP over time. This approach captures spatial differences in NPP growth and reveals the trajectory of productivity-center migration (Guo et al., 2020). The calculation formulas are shown in Equation 5:where x *i* and y *i* are the longitude and latitude coordinates of pixel i, respectively; NPPᵢ is the NPP value of pixel i; x *t* and y *t* are the coordinates of the NPP-weighted centroid in year t; and n is the number of pixels included in the calculation.

x t = ∑ i = 1 n N P P i x i ∑ i = 1 n N P P i y t = ∑ i = 1 n N P P i y i ∑ i = 1 n N P P i (5)

2.3.5 Partial derivative analysis

Partial derivative analysis, originally introduced by McCuen in hydrological dynamics research, has recently been extended to vegetation-change attribution (Zhang et al., 2016; Guo et al., 2020). In this study, pixel-wise multiple linear regression of annual NPP against temperature (T), precipitation (P), and solar radiation (S) was used to estimate the partial responses of NPP to the three climatic variables. The annual rates of change in NPP and each climatic variable were then combined with these partial responses through a chain-rule decomposition. This procedure yields signed, additive contributions that can be compared across pixels. The calculation steps are shown in Equation 6:where dNPP/dt is the interannual rate of change in NPP; T, P, and S denote temperature, precipitation, and solar radiation, respectively. T *con*, P *con*, and S *con* represent the contributions of temperature, precipitation, and solar radiation to interannual NPP variation. The partial derivatives ∂NPP/∂T, ∂NPP/∂P, and ∂NPP/∂S are estimated from the pixel-wise multiple linear regression and quantify the partial responses of NPP to temperature, precipitation, and solar radiation. The terms dT/dt, dP/dt, and dS/dt are the annual rates of change in the three climatic variables. H *con* denotes the residual contribution after the explicitly modeled climatic effects have been removed. This residual may include human activities and other unmodeled non-climatic influences, such as wildfire disturbances, CO2 fertilization, soil properties, or omitted meteorological variables. Because human effects are expected to dominate this residual in the study area, Hcon was interpreted as the contribution of human activities, while recognizing that it is not a pure causal estimate of human activity alone. Each contribution may be positive, indicating an increase in NPP, or negative, indicating a suppression of NPP growth.

d N P P d t ≈ ∂ N P P ∂ T × d T d t + ∂ N P P ∂ P × d P d t + ∂ N P P ∂ S × d S d t + H c o n = T c o n + P c o n + S c o n + H A c o n (6)

For each pixel, the contribution of a climatic variable was calculated as its estimated partial response multiplied by the annual rate of change in that variable. We defined CC *con* as the integrated climatic contribution, calculated as T *con* + P *con* + S *con*, and HA *con* as H *con*. The dominant driver was identified from the signs of CCcon and HAcon in relation to the observed NPP trend. If both contributions had the same sign as the observed change, their relative contributions were normalized according to Table 1; if only one contribution had the same sign as the observed change, that driver was assigned 100% of the relative contribution. This approach quantifies additive attribution and does not explicitly estimate interaction effects among drivers.

TABLE 1. Decision rules used to identify dominant climatic and human-activity contributions to NPP changes in Henan Province.

NPP change classObserved trendDominant driverClimate contributionHuman-activity contributionRelative climate contribution (%)Relative human-activity contribution (%)
Vegetation improvement>0HA<0>00100
>0CC>0<01000
>0CC and HA>0>0C C c o n C C c o n + H A c o n × 100H A c o n C C c o n + H A c o n × 100
Vegetation degradation<0CC<0>01000
<0HA>0<00100
<0CC and HA<0<0C C c o n C C c o n + H A c o n × 100H A c o n C C c o n + H A c o n × 100

3 Results

3.1 Interannual variation of NPP

From 2001 to 2024, annual NPP in Henan ranged from 316.41 to 512.14 g C·m-2·a−1, with a multiyear mean of 486.88 g C·m-2·a−1 (Figure 2). NPP increased significantly at a rate of 4.43 g C·m-2·a−1 (P < 0.01) and reached its highest value in 2024 and its lowest value in 2001. In 2019, NPP declined to 387.66 g C·m-2·a−1, the second-lowest annual value in the series. The pronounced interannual variability indicates that the long-term increase was accompanied by substantial year-to-year fluctuations.

Interannual variation and linear trend of province-wide annual NPP in Henan Province, 2001–2024. The dashed line represents the fitted linear trend, and the fitted equation and R<sup>2</sup> are shown in the plot.
Figure 2. Interannual variation and linear trend of province-wide annual NPP in Henan Province, 2001–2024. The dashed line represents the fitted linear trend, and the fitted equation and R2 are shown in the plot.

NPP also differed substantially among land-use types (Table 2). Mean NPP followed the order woodland (500.88 g C·m-2·a−1) > grassland (482.51) > farmland (403.32) > built-up land (340.11) > water bodies (322.95) > barren land (208.36). Positive long-term trends were observed for all land-use types. The highest rates of increase occurred in grassland (5.57 g C·m-2·a−1) and woodland (4.58 g C·m-2·a−1), followed by water bodies (3.91), farmland (3.65), built-up land (2.84), and barren land (2.31 g C·m-2·a−1).

TABLE 2. Mean annual NPP, NPP range, and trend slopes for each land-use type in Henan Province, 2001–2024.

Land-use typeNPP range (g C·m-2·a-1)Mean annual NPP (g C·m-2·a-1)Slope (g C·m-2·a-1)
Water222.79–405.53322.953.91
Woodland329.11–596.47500.884.58
Grassland342.51–584.45482.515.57
Farmland291.79–484.26403.323.65
Built-up land266.32–411.93340.112.84
Barren land169.61–235.92208.362.31

3.2 Spatial and temporal variation of NPP

Mean NPP displayed a clear spatial gradient, with higher values in western and southern Henan and lower values in eastern and northern areas (Figure 3a). The Funiu Mountains in western Henan and the Tongbai-Dabie Mountains in the south were dominated by woodland and generally had NPP values above 500 g C·m-2·a−1. By contrast, the central and eastern plains, including areas around Zhengzhou, Xuchang, and Zhoukou, were dominated by intensively managed farmland and generally had values of 350–470 g C·m-2·a−1. NPP was also relatively low in northern Henan. Values around Jiyuan, Jiaozuo, northern Xinxiang, and northern Anyang generally ranged from 350 to 420 g C·m-2·a−1, whereas Hebi, central-southern Xinxiang, southern Anyang, and Puyang commonly had values below 350 g C·m-2·a−1. The lowest values were concentrated along major urban corridors, particularly the Beijing–Guangzhou and Lianyungang–Lanzhou railway corridors. The spatial distribution of NPP trends is shown in Figure 3b. Theil-Sen slope ranged from −17.68 to 27.48 g C·m-2·a−1, with stronger positive trends in western than in eastern Henan. Increasing trends covered 94.70% of the vegetated area, including significant increases over 64.41% of the area (Figure 3c). Significant increases were concentrated mainly in central-western and southern Henan. No significant increases accounted for 30.29% of the area and were distributed mainly adjacent to significantly increasing areas and in eastern Kaifeng, Shangqiu, and Zhoukou. Unchanged areas accounted for 2.16%, whereas decreasing trends covered 3.14%, including significant (0.84%) and no significant (2.30%) decreases. Decreasing trends were concentrated primarily along urban corridors and in other areas subject to strong land-use pressure.

Spatial distribution and temporal trend of annual NPP in Henan Province, 2001–2024. **(a)** Mean annual NPP; **(b)** Theil-Sen slope; **(c)** Mann-Kendall trend significance.
Figure 3. Spatial distribution and temporal trend of annual NPP in Henan Province, 2001–2024. (a) Mean annual NPP; (b) Theil-Sen slope; (c) Mann-Kendall trend significance.

Across land-use types (Table 3), the proportion of area with increasing NPP was highest for woodland (97.86%), followed by grassland (92.90%), farmland (92.48%), built-up land (53.67%), water bodies (41.56%), and barren land (35.96%). Woodland, grassland, and farmland therefore showed particularly widespread increases. Water bodies and barren land were dominated by unchanged NPP, accounting for 56.44% and 54.39% of their respective areas. Built-up land and barren land also had the largest proportions of declining NPP, at 9.09% and 9.65%, respectively, whereas the proportions for the other land-use types remained below 3.71%.

TABLE 3. Percentage of each land-use type classified into annual NPP trend categories in Henan Province, 2001–2024.

Land-use typeSignificant decreaseNo significant decreaseUnchangedNo significant increaseSignificant increase
Water0.61%1.39%56.44%13.47%28.09%
Woodland0.49%0.77%0.88%35.69%62.17%
Grassland1.22%2.48%3.39%14.84%78.06%
Farmland0.51%1.97%5.04%32.02%60.46%
Built-up land3.55%5.54%37.24%16.26%37.41%
Barren land4.39%5.26%54.39%8.77%27.19%

3.3 Stability and future trends of NPP

The spatial distributions of the coefficient of variation (CV) and Hurst exponent are shown in Figure 4. CV values ranged from 0.00 to 0.896, with a mean of 0.14 (Figure 4a). Very stable, relatively stable, moderately stable, moderately unstable, and very unstable areas accounted for 4.65%, 6.15%, 54.67%, 27.36%, and 7.17%, respectively. Higher CV values were concentrated in central and northern areas, including parts of Kaifeng, Anyang, Xinxiang, and Puyang, with additional patches in western Sanmenxia, Pingdingshan, and southwestern Xinyang. Lower CV values occurred more frequently in woodland- and farmland-dominated areas and in spatially homogeneous barren, water, and built-up surfaces.

Temporal stability and future trajectory indicators for NPP in Henan Province, 2001–2024. **(a)** Coefficient of variation (CV); **(b)** Hurst exponent; **(c)** classification of future NPP trends.
Figure 4. Temporal stability and future trajectory indicators for NPP in Henan Province, 2001–2024. (a) Coefficient of variation (CV); (b) Hurst exponent; (c) classification of future NPP trends.

The Hurst exponent ranged from 0.00 to 0.88, with a mean of 0.46 (Figure 4b). Areas with H > 0.5 and H < 0.5 accounted for 30.88% and 69.07% of the study area, respectively. When the Theil-Sen slope and Hurst exponent were considered together (Figure 4c), continuous improvement accounted for 28.59%, whereas future degradation accounted for 67.32%. Continuous degradation, future improvement, and unpredictable trajectories accounted for 2.29%, 1.75%, and 0.05%, respectively. This distribution indicates that recent positive NPP trends do not necessarily persist and that a substantial proportion of the study area shows a statistical tendency towards future reversal.

3.4 Migration of the NPP gravity center

The NPP gravity-center coordinates for different years, derived using the gravity-center model, are shown in Figure 5. Over the 24-year period, the NPP gravity center remained within Xiangcheng County, Xuchang City. In 2001, the gravity center was located at (113°31′E, 33°46′N). From 2001 to 2005, it shifted 0.82 km northwestward at a rate of 0.21 km/a; from 2005 to 2013, it moved 13.42 km southwestward at a rate of 1.68 km/a; from 2013 to 2021, it moved 10.83 km northeastward at a rate of 1.35 km/a; and from 2021 to 2024, it shifted rapidly northwestward by 10.47 km at a rate of 3.49 km/a. Over the entire period, the NPP gravity center shifted 4.58 km northwestward (migration rate: 0.20 km/a) and was ultimately located at (113°29′E, 33°48′N), indicating that NPP improvement in the northwestern region was greater than in other areas.

Spatiotemporal migration of the NPP-weighted gravity center in Henan Province, 2001–2024. **(a)** Location of the NPP-weighted gravity center in Henan Province; **(b)** enlarged view of the gravity center in Xuchang City; **(c)** trajectory of the centroid in longitude-latitude space.
Figure 5. Spatiotemporal migration of the NPP-weighted gravity center in Henan Province, 2001–2024. (a) Location of the NPP-weighted gravity center in Henan Province; (b) enlarged view of the gravity center in Xuchang City; (c) trajectory of the centroid in longitude-latitude space.

3.5 Contributions of climatic factors and human activities

The spatial contributions of temperature, precipitation, and solar radiation are shown in Figure 6. Temperature contributions ranged from −9.51 to 10.09 g C·m-2·a−1, precipitation contributions ranged from −4.82 to 8.71 g C·m-2·a−1, and solar-radiation contributions ranged from −4.58 to 7.71 g C·m-2·a−1. Their mean contributions were 1.97, −0.30, and 0.19 g C·m-2·a−1, respectively. Temperature contributed positively across 94.32% of the study area, whereas precipitation contributed negatively across 86.52% of the area. Solar radiation contributed positively across 85.45% of the area. These contrasting spatial responses indicate that individual climatic controls did not operate uniformly across the province.

Spatial contributions of individual climatic factors, integrated climate change, and human activities to NPP change in Henan Province, 2001–2024. **(a)** Temperature; **(b)** precipitation; **(c)** solar radiation; **(d)** integrated climatic contribution; **(e)** human-activity contribution.
Figure 6. Spatial contributions of individual climatic factors, integrated climate change, and human activities to NPP change in Henan Province, 2001–2024. (a) Temperature; (b) precipitation; (c) solar radiation; (d) integrated climatic contribution; (e) human-activity contribution.

The integrated climatic contribution ranged from −9.70 to 10.98 g C·m-2·a−1, with a mean of 1.85 g C·m-2·a−1 (Figure 6d). Positive and negative climatic contributions accounted for 91.90% and 8.10% of the area, respectively. The residual contribution, interpreted primarily as the contribution of human activities, ranged from −8.95 to 16.10 g C·m-2·a−1, with a mean of 2.42 g C·m-2·a−1. Positive and negative residual contributions accounted for 97.37% and 2.63% of the area, respectively. Positive human-activity contributions were concentrated mainly in western and southwestern Sanmenxia, Luoyang, Nanyang, and Xinyang, whereas negative contributions occurred around urban fringes, particularly along major transport and development corridors. At the provincial scale, both the integrated climatic contribution and human-activity contribution were predominantly positive.

3.6 Relative contribution of climate change and human activities

Across the study area, climate change and human activities accounted for 43.19% and 56.81% of total NPP change, respectively (Figure 7). In areas with increasing NPP, the relative contribution of climate change averaged 42.15%, whereas human activities contributed 57.85%. Climate contributions of 20%–60% occurred across 81.95% of improving areas, whereas contributions exceeding 60% covered 8.60%, mainly in the northwestern Taihang Mountains, eastern Shangqiu, and southwestern Nanyang. Human-activity contributions of 40%–80% occurred across 81.95% of improving areas, and contributions exceeding 80% covered 9.45%, mainly in eastern Kaifeng, northern Xinxiang and Puyang, southwestern Nanyang, and southeastern Xinyang. Thus, human influences were generally stronger than climatic influences in areas experiencing NPP improvement.

Relative contributions of climate change and human activities to NPP change in Henan Province, 2001–2024. **(a)** Climate contribution in vegetation improvement areas; **(b)** human-activity contribution in vegetation-improvement areas; **(c)** climate contribution in vegetation-degradation areas; **(d)** human-activity contribution in vegetation-degradation areas.
Figure 7. Relative contributions of climate change and human activities to NPP change in Henan Province, 2001–2024. (a) Climate contribution in vegetation improvement areas; (b) human-activity contribution in vegetation-improvement areas; (c) climate contribution in vegetation-degradation areas; (d) human-activity contribution in vegetation-degradation areas.

In areas with declining NPP (Figures 7c,d), climate change accounted for 67.82% of the relative contribution, whereas human activities accounted for 32.18%. Climate contributions exceeding 80% covered 47.53% of degradation areas and were concentrated mainly around urban fringes and in southern Xinyang; contributions below 40% accounted for 20.85%. Human-activity contributions below 20% occurred across 47.53% of degradation areas, whereas contributions exceeding 80% accounted for 13.46%, primarily around urban fringes.

Across land-use types (Figure 8a), climate change contributed 40.49%–57.52% to NPP improvement. Its contribution was highest for barren land and lowest for water bodies, while contributions for the remaining types ranged from 40.75% to 46.31%. Human activities contributed most strongly to improvement in water bodies (59.51%) and farmland (59.25%) and least strongly in barren land (42.48%); contributions for the other land-use types exceeded 53.69%. In degradation areas (Figure 8b), climate change contributed most strongly to farmland degradation (74.39%) and least strongly to woodland degradation (48.20%), while contributions to the other types ranged from 50.71% to 63.09%. Human activities contributed most strongly to woodland degradation (51.80%) and least strongly to farmland degradation (25.61%).

Relative contributions of climate change and human activities to NPP change across land-use types in Henan Province, 2001–2024. **(a)** Vegetation-improvement areas; **(b)** vegetation-degradation areas.
Figure 8. Relative contributions of climate change and human activities to NPP change across land-use types in Henan Province, 2001–2024. (a) Vegetation-improvement areas; (b) vegetation-degradation areas.

4 Discussion

4.1 Spatiotemporal evolution of NPP

The observed significant increase in NPP (4.43 g C·m-2·a−1) is consistent with earlier studies (Cao et al., 2024; Zhi et al., 2024; Li et al., 2026). The spatial pattern of higher NPP in the west and south and lower NPP in the east and north may reflect combined differences in topography, hydrothermal conditions, and land use (Cao et al., 2024; Li et al., 2026). Higher NPP in the western and southern mountains (>500 g C·m-2·a−1) is attributable to higher elevations, forest and grassland cover, and lower human pressure, whereas lower NPP in the eastern and northern plains is associated with intensive farmland use and urban and economic expansion (Liang et al., 2026).

The observed land-use hierarchy of NPP (woodland > grassland > farmland > built-up land > water bodies > barren land) corresponds to previous reports (Ren et al., 2024; Wang Y. T. et al., 2025). The higher NPP of woodland and grassland, their larger proportions of improving areas, and the northwestward migration of the NPP gravity center are consistent with the effects of ecological restoration programs, such as Taihang Mountains restoration and Grain for Green, on vegetation recovery in the northwestern and western highlands (Song et al., 2022; Wang F. et al., 2025). The high improvement proportion in farmland (92.48%), despite its moderate mean NPP, may be related to agricultural intensification, including improved irrigation, fertilization, and conservation tillage (Lobell et al., 2013; Wang L. et al., 2025). In contrast, the lower improvement (53.67%) and higher degradation (9.09%) in built-up land may indicate that urbanization pressure has locally exceeded the effects of greening efforts (Wang et al., 2022). The lower performance of barren land, with the lowest improvement and highest degradation, is likely due to its intrinsic fragility and high sensitivity to climatic extremes.

4.2 Stability and future trajectories

Although NPP in Henan was generally stable (mean CV = 0.14), larger fluctuations occurred in central and northern Henan and in surrounding urban areas. These fluctuations may be associated with urban expansion and intensive land-use pressure (Du et al., 2019). The mean Hurst exponent was 0.46, indicating that anti-persistent behavior dominated the province, with 69.61% of the area showing a tendency towards future degradation. This result is comparable to observations in the Qinling Mountains and across China (81.98% and 81.54%, respectively; Meng et al., 2026a; 2026b). The widespread anti-persistent behavior suggests that recent greening does not necessarily guarantee long-term stability and that vegetation recovery in Henan remains vulnerable to ongoing environmental and human pressures (Liu et al., 2025). Continued ecological monitoring and targeted management are therefore needed in areas with potential degradation risks.

4.3 Climatic controls on NPP

The predominantly positive contribution of temperature to NPP (mean 1.97 g C·m-2·a−1), especially in the western highlands, is consistent with reports that warming may lengthen the growing season (Guo et al., 2021). In contrast, precipitation had a mean negative contribution of −0.30 g C·m-2·a−1 across most of the province, in agreement with Xiao and Wang (2025). Unlike arid and semi-arid regions, where precipitation is often the primary limiting factor (Jiang S. Z. et al., 2020; Xue et al., 2023; Meng et al., 2026b), Henan’s humid to semi-humid climate and widespread irrigation may buffer vegetation against precipitation variability (Yang and Liu, 2025; Wang et al., 2021). Solar radiation, with a mean positive contribution of 0.19 g C·m-2·a−1, may have promoted photosynthesis and carbon fixation, particularly in the Dabie Mountains, Nanyang Basin, and northern areas (Gu et al., 2002). However, its warming effect could increase evapotranspiration and suppress NPP in water-deficient settings (Zhang et al., 2020). The lower integrated climatic contribution (1.85 g C·m-2·a−1), relative to the contribution of temperature alone, may reflect offsetting effects among climatic factors (Huang et al., 2015).

4.4 Human activities versus climate change

The greater contribution of human activities (56.81%) than climate change (43.19%) to overall NPP dynamics suggests that human management may play an important role in Henan, consistent with earlier work (Chen et al., 2017; Xiao and Wang, 2025). In improvement areas, the relatively high human contribution (57.85%) in the eastern plains and northern regions may be associated with Grain for Green, the South-to-North Water Diversion ecological corridor, and high-standard farmland construction (Song et al., 2022; Wang F. et al., 2025; Huang et al., 2026). The high human contributions to farmland (59.25%) and water bodies (59.51%) indicate the effects of agricultural management and wetland restoration, respectively (Zhang S. et al., 2024). Barren land, with the lowest human contribution (42.48%), may remain more strongly constrained by climatic factors.

In degradation areas, climate change was the dominant factor (67.82%), with urban fringes and southern Xinyang showing the largest negative climatic effects, potentially exacerbated by urban heat islands and altered local hydrology (Kalnay and Cai, 2003). The particularly high climate contribution to farmland degradation (74.39%) indicates the vulnerability of major grain-producing areas to extreme climate events (Wu et al., 2023; Zhi et al., 2024). Similarly, the high climate contributions to the degradation of barren land (63.09%) and water bodies (61.11%) may reflect direct climatic forcing on these unmanaged land-cover types (Igboeli et al., 2025). The lower human contribution to farmland degradation (25.61%) indirectly supports the buffering effect of current farmland management. The substantial human contributions to both improvement and degradation in built-up land (53.69% and 49.29%, respectively) may reflect the dual and potentially conflicting effects of urbanization (Chen et al., 2024), requiring more nuanced spatial planning.

5 Limitations and future research

Several limitations should be acknowledged. First, partial derivative analysis assumes independence among climatic variables, which may underestimate interactive effects and introduce uncertainty into the attribution (Zhang et al., 2016; Guo et al., 2020). Second, the 500 m climatic layers represent coarser ERA5-Land information on a common analysis grid; therefore, fine-scale climatic interpretations should be made cautiously. Resampling cannot create new spatial information and may smooth local climatic gradients. Where available, future studies could use downscaled climate datasets to better capture local-scale variability. Third, only temperature, precipitation, and solar radiation were considered. Other potentially influential meteorological factors, such as wind speed and relative humidity, as well as topographic variables, CO2 fertilization, and soil properties, were not included. Future studies should integrate higher-resolution, multi-source data (e.g., nighttime lights, soil attributes, and high-resolution meteorological grids), and use the Geodetector model and advanced machine-learning algorithms, such as Random Forest and XGBoost, to better capture the nonlinearities and interactions among drivers (Kumar et al., 2025; Zhang Y. L. et al., 2026). This would improve the reliability and explanatory power of attribution analyses.

6 Conclusion

Using MODIS NPP data from 2001 to 2024, this study quantified the spatiotemporal dynamics, temporal stability, potential future trajectories, spatial redistribution, and climatic and human activities controls of vegetation net primary productivity in Henan Province. The main findings are as follows:

  • Over the 24-year period, Henan’s NPP exhibited a significant and widespread greening trend (4.43 g C·m-2·a−1), characterized by a distinct pattern of high values in the west and south and low values in the east and north. Land-use type strongly influenced both NPP levels and trends: forest, grassland, and farmland showed widespread improvement (>92%), whereas built-up land exhibited the lowest improvement (53.67%) and the second-highest degradation (9.09%), indicating emerging urbanization pressure on regional productivity.
  • NPP exhibited limited interannual variability overall, with a mean CV of 0.14. However, Hurst-exponent analysis indicated that a large proportion of the region exhibited anti-persistent behavior and a potential risk of future degradation, particularly in western high-altitude areas, the Anyang-Puyang region in the north, and peri-urban areas. This statistical tendency towards reversal highlights the need to distinguish recent greening from long-term persistence when assessing ecosystem resilience.
  • Temperature and solar radiation contributed predominantly positively to NPP, whereas precipitation contributed predominantly negatively. The integrated climatic contribution averaged 1.85 g C·m-2·a−1, which was lower than the contribution of temperature alone, indicating partial compensation among climatic drivers and emphasizing the importance of multi-factor attribution.
  • Human activities contributed more to overall NPP changes than climate change (56.81% vs. 43.19%), indicating the effects of ecological restoration and agricultural modernization. However, this human dominance was conditional on the direction of NPP change. Human activities dominated vegetation improvement (57.85%), particularly in farmland and water bodies (>59%), whereas climate change was the dominant driver of degradation (67.82%), with farmland being the most vulnerable to climatic extremes (74.39%). Built-up land exhibited a dual character, with substantial human contributions to both improvement (53.69%) and degradation (49.29%), reflecting the coexistence of urban greening and land-use intensification pressures.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. The authors confirm that generative AI was used solely for language polishing and grammatical refinement of the manuscript. No generative AI was used for data analysis, interpretation, figure creation, or the generation of scientific conclusions. The authors have reviewed and verified all content and take full responsibility for the accuracy, originality, and integrity of the work presented.

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Footnotes

Edited by: Vinicius Londe, Independent Researcher, Bothell, United States

Reviewed by: Qing Meng, Shangluo University, China

Sanjay Kumar, CEPT University Faculty of Technology, India

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Students Innovation and Entrepreneurship Training Program Project of Anyang Normal University (202610479144), the China Scholarship Council (202508410156), the Henan Provincial Natural Science Foundation of China (252300420280), the Training Program for Young Backbone Teachers in Higher Education Institutions of Henan Province (2023GGJS125), and the High-level Talents International Cultivation Program of Henan Province (GCC2025029).

Data availability

Publicly available datasets were analyzed in this study. This data can be found here: MODIS NPP and MCD12Q1 land cover data: https://lpdaac.usgs.gov (Product IDs: MOD17A3HGF.006, MCD12Q1.006) ERA5-Land climate reanalysis data: https://www.ecmwf.int/en/era5-land DEM data: https://www.geodata.cn.

Author contributions

ZY: Data curation, Conceptualization, Writing – original draft, Methodology, Formal Analysis. JL: Formal Analysis, Methodology, Writing – review and editing, Data curation. ZZ: Visualization, Writing – original draft, Methodology, Data curation. JA: Writing – review and editing, Visualization, Methodology, Data curation. RZ: Data curation, Writing – review and editing. HL: Validation, Writing – review and editing. LY: Writing – review and editing. MC: Supervision, Conceptualization, Writing – review and editing, Project administration.

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Citation

Yuan, Z., et al. (2026). Effects of climate change and human activities on vegetation net primary productivity: a case study of Henan Province, China. Frontiers in Environmental Science, Volume 14. https://doi.org/
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14 - 2026

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Copyright © 2026 Yuan, Lu, Zhang, An, Zhang, Li, Yu and Cai.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Zhigao Zhang; Maotang Cai

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