NPP 时空 MGWR

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts.

Grasslands, a vital ecosystem and component of the global carbon cycle, play a significant role in evaluating ecosystem health and monitoring the global carbon balance. In this study, based on the Carnegie–Ames–Stanford Approach (CASA) model, we estimated the Net Primary Productivity (NPP) of grasslands in northern Shaanxi from 2000 to 2020. Employing trend analysis, stability analysis, multiple regression analysis, and residual analysis, the research examined the dynamic changes of grassland NPP and its response to climatic and human factors. Key findings include: (1) Grassland NPP showed a significant increasing trend during 2000–2020, with high-coverage grasslands showing a higher rate of increase than medium and low-coverage grasslands. (2) Most grasslands (>90%) exhibited unstable growth and high NPP fluctuation. (3) While temperature, precipitation, and radiation undulate, the trends were not significant. Rainfall and radiation emerged as dominant factors affecting NPP, with temperature suppressing NPP increase to some extent. (4) Policies like returning farmland to grassland had a positive impact on grassland recovery, vegetation productivity, and regional ecosystem health.

… 传统森林 NPP 估算方法依赖样地测量,难以在较大 区域开展森林 NPP 的长时间动态… NPP 数值估算的方法逐渐呈现多 元化趋势,利用遥感影像结合 NPP 估算模型[6] 可以较为 精准地进行 NPP …

The study of vegetation net primary productivity (NPP) is essential in the Yanshan–Taihang Mountain Ecological Conservation Zone (YTECZ). Serving as an ecological security barrier for the Beijing–Tianjin–Hebei region, understanding the spatiotemporal dynamics and drivers of NPP in the YTECZ is fundamental for supporting effective sustainable development policies. Utilizing MODIS NPP, climatic data (temperature and precipitation), and the Human Footprint Index (HFP, a comprehensive metric of anthropogenic pressure), this study employed univariate linear regression, ArcGIS spatial analysis, and the Geographical Detector to investigate the spatiotemporal patterns and drivers of vegetation NPP in the YTECZ from 2004 to 2023 and to project its future trends through time series analysis. Our findings reveal a significant fluctuating upward trend in vegetation NPP over the 21-year period (mean annual increase: 4.58 g C·m−2), displaying a distinct spatial gradient characterized by higher values in western and northern sectors relative to eastern and southern areas. The interannual variability of vegetation NPP was primarily dominated by precipitation fluctuation, while its spatial heterogeneity was jointly driven by vapor pressure deficit (VPD) and temperature. Notably, human activities exhibited significant explanatory power on NPP’s spatial pattern, and their interaction with climatic factors (e.g., VPD) resulted in non-linear enhancements. Future projections suggest that the current increasing trend is unlikely to be sustained in the long term, indicating substantial uncertainty in vegetation carbon sequestration patterns. This study provides critical insights into vegetation response mechanisms to global change drivers, offering a scientific foundation for ecological management strategies toward sustainable development in the YTECZ.

Investigating how the productivity dynamics of planted forests vary over time is important for understanding the resilience of forests against disturbance and for maximizing ecological restoration and replanting efforts. In this study, the patterns of interannual variability in net primary production (NPP) were analyzed for planted forests as indicated by the inverse of the coefficient of variation (ICV) time series at a ten-year moving window on the Loess Plateau, China, from 2000 to 2021. The spatial–temporal patterns were defined based on the increase or decrease trend obtained using the ordinary least squares method between abrupt change points performed by a Mann–Kendall test in an ICV time series, as follows: only one linear trend, increase (LI), and decrease (LD); at least two trends, increase firstly and decrease lastly (ID) and decrease firstly and increase lastly (DI); and other trends. The results showed that 82.74% of the ICV on the Loess Plateau displayed LD and ID patterns, indicating an increasing variability of forest productivity in this region. Overall, 73.83% of the ICV had a lower degree of rate decrease in the last phase than during the initial increase. Thus, the variability was in an early stage of increasing degree. The ICV time series showed an LI pattern in the eastern Gansu and the southern Shanxi, indicating a decreased variability, due partly to the improved forest restoration. When the plantation age was considered, the newly planted forests (less than 19 a) exhibited a decreasing variability, indicating the proactive role of forest management and restoration in averting environmental disruptions in dry environments.

To host the 2022 Winter Olympics, Beijing and Zhangjiakou implemented extensive ecological restoration projects, improving the ecological quality of the region. However, detailed evidence of long-term spatiotemporal dynamics in vegetation productivity remains limited. This study employed the Carnegie–Ames–Stanford Approach (CASA) to estimate the vegetation Net Primary Productivity (NPP) in the Beijing–Zhangjiakou region from 2004 to 2023, utilizing 250 m monthly NDVI data. The 30 m resolution China Land Cover Dataset (CLCD) was incorporated to mask non-vegetated pixels and refine the vegetation mask, reducing mixed-pixel effects. Spatiotemporal variations, seasonal change-point detection, interannual stability, and trend persistence were analyzed across administrative regions and land cover types. Results indicate pronounced spatial heterogeneity in NPP, with persistently high values in forest-dominated western and northern Beijing and northeastern Zhangjiakou, and lower values concentrated in Beijing’s built-up and cropland-dominated southeastern plain. Pixel-level boxplots suggest stronger intra-regional variability in Beijing than in Zhangjiakou. Across landcover types, forests generally maintain the highest NPP, while grasslands are relatively lower. Boxplots further show that shrubs exhibit the highest variability, with all types showing right-skewed distributions. Annual mean NPP increased significantly for the entire region, Beijing, and Zhangjiakou, with interannual increase rates of 3.57, 1.56, and 4.53 gC·m−2·yr−2, respectively; the lowest values occurred in 2007 and the highest in 2022. Trend maps and category statistics consistently suggest that positive trends dominate most of the region and expanded slightly during 2014–2023. BEAST analysis suggests a stable seasonal NPP cycle with no significant seasonal change points. CV-based assessment indicates generally high to extremely high stability, whereas low-stability zones are mainly associated with urban expansion areas, surrounding croplands, and parts of Zhangjiakou grasslands. Hurst results suggest that persistently increasing trends cover more than 90% of the study area, while persistently decreasing trends account for about 5.25% and are primarily linked to Beijing’s expansion zones.

This study comprehensively analyzed the compounded effects of climatic factors and non-climatic factors on vegetation dynamics in the northern Shaanxi Loess Plateau region in China. The objective was to provide robust scientific insights and a solid theoretical framework to support the long-term stability and sustainable development of the local ecosystem. The temperature vegetation dryness index was used to improve the water stress factor of the CASA model, so as to estimate the NPP of vegetation on the Loess Plateau of northern Shaanxi from 2000 to 2020. The temporal and spatial change characteristics of vegetation NPP and its relationship with climatic factors were analyzed using the coefficient of variation, the Mann–Kendall test of significance, and second-order partial correlation analysis. The partial derivative residual trend method was used to isolate the specific impacts of climatic factors and non-climatic factors on vegetation NPP. The results indicate the following: (1) The vegetation NPP shows a notable upward trend, with an annual growth rate of 9.4195 gC·m−2·a−1 and a long-term average of 269.71 gC·m−2, with the spatial distribution showing markedly high south, low north, and latitudinal zonation characteristics. (2) Vegetation NPP exhibits positive correlations with temperature, precipitation, and solar radiation. Among these factors, precipitation shows the strongest correlation with variations in vegetation NPP. (3) Non-climatic factors are the main factor affecting vegetation NPP across most parts of the study area, which is greater than the effect of selected climatic factors, and human activities may be the key component within non-climatic factors.

Vegetation Net Primary Productivity (NPP) plays a crucial role in terrestrial carbon sinks and the global carbon cycle. Investigating the spatiotemporal dynamics and influencing factors in the Yangtze River Delta (YRD) region can furnish a solid scientific foundation for green, low-carbon, and sustainable development in China, as well as a reference for other rapidly urbanizing regions. This study focuses on the YRD region as an illustration and utilizes the Carnegie–Ames–Stanford Approach (CASA model) to quantify NPP in this region from 2000 to 2018. Investigation into the spatiotemporal dynamics and influencing factors was conducted using Theil–Sen median trend analysis and scenario analysis. The results indicate that the NPP in the YRD region from 2000 to 2018 exhibited pronounced spatial differentiation characteristics, typically exhibiting a spatial distribution pattern of being high in the south and low in the north, high in the west and low in the east. Additionally, the expansion of built-up areas and the reduction in cultivated land have the potential to reduce NPP in the YRD region. Moreover, the influence of land-use and land-cover change (LULCC) is anticipated to be relatively limited compared to that of climate change. Furthermore, changes in precipitation were found to be positively correlated with changes in NPP, with the effect being relatively more pronounced. The correlation between temperature and NPP demonstrated spatial differentiation, with a mainly positive correlation in the central and southern parts of the YRD and a mainly negative correlation in the northern part. Changes in solar radiation had a negative correlation with changes in NPP. Based on these results, it is recommended that local governments strictly enforce urban development boundaries and manage the disorderly expansion of built-up areas, enhance the regional irrigation infrastructure, and address air pollution, so as to ensure the necessary conditions for the growth of vegetation, reduce greenhouse gas emissions, and control regional temperature rises. This study can provide stronger evidence for revealing the influencing mechanisms of NPP through the control of impact conditions and the exclusion of confounding factors via scenario analysis. The policy implications can offer insights into NPP enhancement and environmental management for the YRD and other rapidly urbanizing regions.

The rapid development of the social economy and the continuous change in land use have greatly altered the ecological risk of the regional landscape. This study focused on the Yangtze River Basin in China and aimed to examine the temporal and spatial variation characteristics of landscape ecological risk (LER) over a period of 34 years (1982–2015), after determining the optimal sub-watershed scale. Based on the conditional probability framework, the non-linear response of NPP to LER was revealed. Finally, the primary driving factors of LER were explored, and additional potential causes for changes in NPP were discussed. The study findings indicated that the mean annual LER of the Yangtze River Basin exhibited a spatial distribution characterized by high values observed in the western regions and low values in the eastern regions at the optimal sub-basin scale. Specifically, 30.56 and 22.22% of the sub-basins demonstrated a significant upward and downward trend in annual LER, respectively (P < 0.05). The spatial distribution pattern of the mean annual NPP demonstrated high values in the middle region and low values in the western area, with annual NPP significantly increasing in 94.44% of the sub-basins (P < 0.05). The relationship between annual NPP and annual LER was found to be non-linear, indicating that higher annual LER results in a higher probability of median and high values of annual NPP from the perspective of watershed average. Furthermore, climate factors emerged as the main influencing factor of the NPP. Based on these discoveries, upcoming endeavors should concentrate on optimizing landscape formations and executing a judicious distribution of plant species.

The advent of a range of high-precision NPP products, including MODIS NPP, MOD17 NPP, and GIMMS NPP, has sparked growing interest in the study of Earth’s ecosystems. In order to enhance comprehension of ecosystem health, in order to facilitate the development of rational resource management and environmental conservation policies, this investigation employs the MOD17A3 dataset to analyze historical variations in Net Primary Productivity (NPP) within Shanxi Province from 2001 to 2020, while also exploring future trends. The Theil–Sen median trend analysis and Mann–Kendall test are commonly used methods for analyzing time series data, employed to study the spatiotemporal trends and variations in NPP. The Grey Wolf Optimization–Support Vector Machine (GWO–SVM) model combines optimization algorithms and machine learning methods, enhancing the predictive capacity of the model for future NPP time series changes. Conversely, the Hurst exponent utilizes historical NPP trends to assess the persistence characteristics of NPP and predict future spatial variations in NPP. This study additionally investigates the natural driving factors of NPP using the Geographic Detector approach. The key findings of this study are as follows. (1) Overall, NPP in Shanxi Province exhibits a fluctuating upward trend from 2001 to 2020, with an average value of 206.278 gCm−2a−1. Spatially, NPP exhibits a northwest–low and southeast–high pattern, with significant spatial heterogeneity and considerable variability. (2) The average Hurst exponent is 0.86, indicating a characteristic of strong persistence in growth in future NPP. Regions with strong or higher persistent growth account for 95.54% of the total area. (3) According to the CMIP6 climate scenarios, NPP is projected to gradually increase from 2025 to 2030. (4) The interactive effects between natural factors contribute more to NPP variations than individual factors, with the rainfall–elevation interaction having the highest contribution percentage.

Net Primary Productivity (NPP) is a key indicator of terrestrial ecosystem functioning and a major regulator of the global carbon cycle. Yet, its interannual fluctuations and seasonal drivers remain unclear. Using Theil-Sen trend analysis, Mann-Kendall test, R/S analysis, and the Geographically and Temporally Weighted Regression (GTWR) model, this study examined the spatiotemporal dynamics and driving mechanisms of seasonal NPP in the Huaihe River Economic Belt from 2010 to 2021. Results show a general upward trend across all seasons, with the strongest increase in winter. Spatially, NPP decreased from southeast to northwest, with coastal high-value areas contracting seasonally and distinct differences between mountainous and hilly regions. Seasonal patterns revealed clear heterogeneity: spring, summer, and autumn were dominated by stability or improvement with localized degradation, while winter displayed a stable north-south differentiation. Soil moisture emerged as the dominant driver, with multiple factors exerting synergistic effects on seasonal NPP dynamics. This study provides scientific insights to support ecological management and the pursuit of carbon neutrality in the Huaihe River Economic Belt.

Introduction Net primary productivity (NPP) is an important indicator used to characterize the productivity of terrestrial ecosystems. The spatial distribution and dynamic change in NPP are closely related to regional climate, vegetation growth and human activities. Studying the spatiotemporal dynamics of NPP and its influencing factors plays a vital role in understanding ecosystem carbon sink capacity. Methods Based on MODIS-NPP data, meteorological data, and land use data from 2000 to 2020, we analyzed the spatiotemporal variation characteristics and influencing factors of NPP in the middle reaches of the Yellow River (MRYR) by using unary linear regression analysis, third-order partial correlation analysis, and Sen+Mann-Kendall trend analysis. Results The results showed that the annual average NPP of the MRYR was 319.24 gCm-2a-1 with a spatially decreasing trend from the southern part to the northern part. From 2000 to 2020, the annual average NPP experienced a fluctuating upward trend at a rate of 2.83 gCm-2a-1, and the area with a significant upward trend accounted for 87.68%. The NPP of different land use types differed greatly, in which forest had the greatest increase in NPP. Temperature had a negative correlation with NPP in most parts of the MRYR. Water vapor pressure promoted the accumulation of NPP in the northwestern MRYR. The areas with a positive correlation between NPP and water vapor pressure accounted for 87.6%, and 20.43% of the MRYR area passed the significance test of P< 0.05. Conclusion The results of the study highlight the impact of climate factors and land-use changes on NPP and provide theoretical guidance for high-quality sustainable development in the MRYR.

Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m−2. Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m−2·a−1. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect.

Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient of variation, Hurst index, and land-use and land-cover change (LUCC) transition matrix, to conduct a thorough study of NPP variations in the Shandong Hilly Plain (SDHP) region. Furthermore, the geographic detector method was used to investigate the synergistic effects of meteorological changes and human activities on NPP in this region. Between 2000 and 2020, the vegetation NPP in the SDHP exhibited an average increase rate of 0.537 g C·m−2·a−1. However, the fluctuation in mean annual NPP, ranging from 203 to 230 g C·m−2·a−1, underscores an uneven growth pattern. Significant regional disparities are evident in vegetation NPP, gradually ascending from the southeast to the northwest and from the coastal areas to inland regions. The average Hurst index for the entire study area stands at 0.556, indicating an overall sustained growth trend in the time series of SDHP vegetation NPP. The vegetation NPP changes in SDHP can be well explained by climate variables (mean annual temperature, mean annual precipitation) and human activities (LUCC, night light index); of these, LUCC (q = 0.684) has the highest explanatory power on the impact of NPP and is a major influencing factor. This study deepens the understanding of the driving factors and patterns of vegetation’s dynamic response to climate change and human activities in the SDHP region. At the same time, it provides valuable scientific insights for improving ecosystem quality and promoting the carbon peaking and carbon neutrality goals.

As important manifestations of ecosystem function, clarifying the relationships among regional net primary productivity (NPP), climate change, and human activities is an urgent topic to explore in the context of global climate change and the "dual-carbon" strategy. On the basis of Moderate Resolution Imaging Spectroradiometer (MODIS) NPP data and meteorological and land cover data, we quantitatively investigated the spatial and temporal changes in NPP in different vegetated ecosystems in Guizhou Province and their responses to the driving factors via Theil–Sen trend, multiple linear regression, and correlation analyses. The results revealed that (1) spatially, the area of Guizhou Province where NPP showed an increasing trend accounted for 51.44%, which was much larger than the area where the decreasing trend accounted for 6.42%. Temporally, the NPP in Guizhou Province showed a fluctuating increasing trend, with a rate of 3.65 gC/(m 2 ·a). Under the influence of climate change and human activities, the NPP of all ecosystems showed a fluctuating increasing trend, among which the grassland ecosystem presented the most significant increasing trend in NPP, with a rate of 5.91 gC/(m 2 ·a). (2) Climate change has had a dual effect on NPP in Guizhou Province, but the overall effect has been one of facilitation. Among the factors, temperature and precipitation were positively correlated with NPP at percentages of 82.83% and 72.8%, respectively, with a greater facilitating effect than an inhibitory effect. Sunshine hours and relative humidity were negatively correlated with NPP, accounting for 67.9% and 53.39% of the area, respectively, and the inhibitory effect was slightly greater than the promotional effect. Among the ecosystems in Guizhou, farmland ecosystems were the most significantly affected by climate change. (3) Human activities played a dual role in NPP in Guizhou, but their overall role was that of facilitation. The transformation of forestland and cropland was the main factor influencing the increase in NPP. Among the ecosystems in Guizhou, grassland ecosystems were the most significantly affected by human activities.

ABSTRACT The Daning River Basin is a typical representative of the ‘mountain forest’ in the Three Gorges Reservoir (TGR) area of the Yangtze River. In recent years, with the completion of the Three Gorges Project, the local vegetation has degraded, soil erosion has become severe, and there is an urgent need to assess the environmental quality. Data were fused using the MODIS Normalized Difference Vegetation Index (NDVI) (250 m) and Landsat NDVI (30 m) through an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to obtain ESTARFM NDVI (30 m). This data was then entered into the Carnegie Ames Stanford Approach (CASA) model, along with meteorological and land use data, to calculate vegetation net primary productivity (NPP) and trends in the Daning River Basin. The detection of drivers with a high impact on vegetation NPP was done using a Geodetector. The results show that: (1) In terms of spatial and temporal changes, the annual average NPP of the watershed during the 13 years from 2008 to 2020 generally showed an upward trend, with the average yearly vegetation NPP being 512.33gC·m−2·a−1, exhibiting a low southwest to surrounding increasing trend along the river. (2) The spatial and temporal variation of vegetation NPP is influenced by several factors synergistically, with elevation, temperature, and distance from settlements being the dominant factors. The interaction of these two factors can enhance the explanatory power of vegetation NPP. Through the estimation of vegetation NPP and the analysis of influencing factors in the Daning River Basin, this study can provide a reference for the ecological restoration and management of vegetation NPP in small watersheds under similar environments.

Weather change has a great impact on vegetation growth restoration and ecosystem service function, resulting in significant changes in vegetation net primary productivity (NPP). Therefore, based on MOD17A3 NPP data and meteorological data, this study used the slope of a one-dimensional linear regression equation, Spearman correlation analysis method, and geographical detector model to reveal the spatial and temporal evolution characteristics of NPP in the Ordos section of the Yellow River Basin from 2000 to 2021 and the impact of weather change on NPP. Results: (1) NPP increased from 25.4 gC/m2 in 2000 to 60.3 gC/m2 in 2021. The NPP of vegetation in the northeastern and southern parts of the study area showed a significant increasing trend. (2) From 2000 to 2021, the evaporation showed a fluctuating downward trend, and the relative humidity, temperature, wind speed, surface temperature, and precipitation showed a fluctuating upward trend. (3) Evaporation is the most important factor hindering the growth of NPP. Precipitation, wind speed, and temperature played an important role in promoting NPP, and the average correlation coefficients were 0.62, 0.33, and 0.15, respectively. Relative humidity and surface temperature can promote NPP, but the effect is not significant. (4) The interaction results showed that the combination of temperature and precipitation, wind speed and precipitation, wind speed and temperature, precipitation and evaporation, and precipitation and relative humidity could effectively improve NPP. The interaction of climatic factors has a significant effect on the change of NPP in the Ordos section of the Yellow River Basin. The results provide a strong reference for ecological protection and restoration, the realization of dual carbon goals, and sustainable development in the Yellow River Basin.

The net primary productivity (NPP) of vegetation is an important indicator used to evaluate the quality of terrestrial ecosystems and characterize the carbon balance of ecosystems. In this study, the spatiotemporal distribution and dynamic change in NPP in Africa from 1981 to 2018 were analyzed using the long time series data of NPP. The results of the trend and fluctuation analysis showed that the NPP in the Sahara arid region in northern Africa and the arid region in South Africa exhibited a significant reduction and a high degree of fluctuation; most of the NPP in the tropical rainforests in central Africa and the deciduous broadleaved forests and deciduous needle-leaved forests on the north and south sides of the tropical rainforests increased and showed a low degree of fluctuation; the Congo basin, Gabon, Cameroon, Ghana, Nigeria, Tanzania, and other regions were affected by human activities, while the NPP in these regions exhibited a significant reduction and a high degree of fluctuation. Anomaly analysis showed that the NPP in Africa generally exhibited a slow upward trend during the period from 1981 and 2018. The trend was basically consistent in different seasons, and can be segmented into three phases: (1) a phase of descent from 1981 to 1992, with the NPP below the average value in most years; (2) a phase of steady growth from 1993 to 2000, reaching a peak in 2000; (3) a phase of fluctuations from 2001 to 2018, where the NPP value was above the average value in all years except 2015 and 2016, when the NPP value was low due to abnormally high temperatures and drought. The Mann–Kendall test further showed that the annual and seasonal NPP in Africa exhibited a significant upward trend, and the mutation time points occurred around 1995. The wavelet time series analysis revealed obvious periodic changes in the time series of NPP in Africa. The annual and seasonal NPP showed clear oscillations on time scales of 7, 20, 29, and 55 years. The 55-year period had the strongest signal, and was the first main period. The study can provide a scientific gist for the sustainable development of environmental ecology, agricultural production, and the social economy in Africa.

The dominant influencing factors of changes in vegetation NPP and the relative roles of climate–human factors on the Qinghai–Tibet Plateau (QTP) differ between historical periods and are unclear. Therefore, there is an urgent need to systematically and quantitatively analyze the evolution process of the QTP’s ecosystem pattern and the driving factors of this process. Based on MOD17A3H and meteorological data, the Miami model, correlation analysis, and the residual coefficient method were used to investigate the spatiotemporal patterns of changes in vegetation NPP on the QTP from 2000 to 2020. We then quantitatively distinguished the relative roles of climate change and human activity in the process of vegetation NPP change during different historical periods. The results show the following: (1) From 2000 to 2020, zones with increasing vegetation NPP (10–30%) were the most widely distributed, and were mainly located in the Three-Rivers Headwater Region and the northern part of the Hengduan Mountains. (2) From 2000 to 2020, zones with a significant positive correlation between vegetation NPP and annual precipitation were mostly distributed in the northeastern QTP and the Three-Rivers Headwater Region, while zones with a positive correlation between vegetation NPP and annual average temperature were mostly located in southern Tibet. Zones with a significant positive correlation between NPP and annual sunshine hours were mainly distributed in the southeastern part of the QTP and the southern part of the Tanggula Mountains. In contrast, zones with a significant positive correlation between NPP and accumulated temperature (>10 °C) were mainly concentrated in the northern and eastern parts of the QTP. (3) During different historical periods, the relative roles of climate–human factors in the process of vegetation NPP change on the QTP had obvious spatiotemporal differences. These results could provide scientific support for the protection and restoration of regional ecosystems on the QTP.

Net primary productivity (NPP), a key indicator of terrestrial ecosystem quality and function, represents the amount of organic matter produced by vegetation per unit area and time. This study utilizes the MOD17A3 NPP dataset (2001–2022) to analyze the spatio-temporal dynamics of NPP in Xinjiang and projects future trends using Theil-Sen trend analysis, the Mann–-Kendall test, and the Hurst Index. By integrating meteorological data, this study employs partial correlation analysis, the Miami model, and residual analysis to explore the driving mechanisms behind NPP changes influenced by climatic factors and human activities. The results indicate that: (1) The average NPP in Xinjiang has increased over the years, displaying a spatial pattern with higher values in the north and west. Regions with increasing NPP outnumber those with declining trends, while 75.18% of the area shows un-certain future trends. (2) Precipitation exhibits a stronger positive correlation with NPP compared to temperature. (3) Climate change accounts for 28.34% of the variation in NPP, while human activities account for 71.66%, making the latter the dominant driving factor. This study aids in monitoring ecological degradation risks in arid regions of China and provides a scientific basis for developing rational coping strategies and ecological restoration initiatives.

Against the backdrop of global climate change and rapid urbanization, understanding the spatiotemporal dynamics and driving mechanisms of vegetation net primary productivity (NPP) is critical for ensuring regional ecological security and achieving carbon neutrality goals. This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA) and integrates multi-source remote sensing data with socioeconomic statistics. By combining interpretable machine learning (XGBoost-SHAP) with multiscale geographically weighted regression (MGWR), and incorporating Theil–Sen trend analysis and Mann–Kendall significance testing, we systematically analyze the spatiotemporal variations in NPP and its multiscale driving mechanisms from 2001 to 2020. The results reveal the following: (1) Total NPP in the YRDUA shows an increasing trend, with approximately 24.83% of the region experiencing a significant rise and only 2.75% showing a significant decline, indicating continuous improvement in regional ecological conditions. (2) Land use change resulted in a net NPP loss of 2.67 TgC, yet ecological restoration and advances in agricultural technology effectively mitigated negative impacts and became the main contributors to NPP growth. (3) The results from XGBoost and MGWR are complementary, highlighting the scale-dependent effects of driving factors—at the regional scale, natural factors such as elevation (DEM), precipitation (PRE), and vegetation cover (VFC) have positive impacts on NPP, while the human footprint (HF) generally exerts a negative effect. However, in certain areas, a dose–response effect is observed, in which moderate human intervention can enhance ecological functions. (4) The spatial heterogeneity of NPP is mainly driven by nonlinear interactions between natural and anthropogenic factors. Notably, the interaction between DEM and climatic variables exhibits threshold responses and a “spatial gradient–factor interaction” mechanism, where the same driver may have opposite effects under different geomorphic conditions. Therefore, a well-balanced combination of land use transformation and ecological conservation policies is crucial for enhancing regional ecological functions and NPP. These findings provide scientific support for ecological management and the formulation of sustainable development strategies in urban agglomerations.

Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB.

Grassland ecosystems are essential components of the global ecosystem. They may efficiently reduce CO2 concentrations in the atmosphere and play a vital role in mitigating climate change. The objectives of this study were to reveal the spatial distribution features of net primary production (NPP) and net ecosystem productivity (NEP) under climate change in the Inner Mongolia grassland ecosystem, China, and to devise effective management strategies for grassland ecosystems. Based on the multiscale geographically weighted regression (MGWR) model, this study investigated the spatial variation features of NPP and NEP along with their driving factors. The results showed the following: (1) The annual average NPP in the Inner Mongolia grassland ecosystem was 234.22 gC⋅m−2⋅a−1, and the annual average NEP was 60.31 gC⋅m−2⋅a−1 from 2011 to 2022. Both measures showed a spatial pattern of high values in the northeast and low values in the southwest, as well as a temporal pattern of high values in summer and low values in winter. (2) The normalized difference vegetation index (NDVI) and solar radiation had promoting effects on NPP, where NDVI had the largest significant positive correlation area. In addition, precipitation and temperature on the influence of NPP were significantly negative with a larger area. (3) The area with a significant positive correlation of NDVI, solar radiation, and precipitation on NEP was larger than that with a significant negative correlation, while the area with significant negative correlation of temperature was larger. This study used the MGWR model to explore the relationship between NPP, NEP, and multiple factors. The results showed regional variation in NPP and NEP under the combined effect of various drivers. This contributes to a better understanding of carbon sinks under climate change in the Inner Mongolia grassland ecosystem.

The Taohe River Basin is an essential ecological function area in the upper reaches of the Yellow River. Understanding the intricate trade-offs and synergies between ecosystem services (ESs) and exploring the impact of different factors are essential for achieving win–win outcomes in ecosystem management and socioeconomic development. The role of impact factors on the relationship between ESs, nevertheless, is more challenging to spatialize. This study used different models to estimate the net primary productivity (NPP), water yield (WY), and soil conservation (SC), and analyzed synergies and trade-offs between Ess. The spatial heterogeneity of the effects of natural and social factors on the relationships between Ess was explored using a geographic detector and a multi-scale geographically weighted regression (MGWR) model. The results show that: (1) NPP, WY, and SC all exhibit a rising trend, with multi-year averages of 488.99 gC/m2, 157.29 mm, and 1441.51 t/hm2, respectively; (2) NPP–WY and NPP–SC exhibit trade-offs in the majority of regions, while WY–SC are primarily synergistic in the upper and middle reaches, and they have the highest percentage of cropland, forest, and grassland; and (3) precipitation (PRE) has the greatest impact on the trade-off between NPP–WY and NPP–SC in the upper and middle reaches, and the gross domestic product (GDP), population density (POP), and distance from cropland (CROP) are the primary factors determining the synergy between NPP and WY in the lower reaches of the Loess Plateau cropping sector. PRE, digital elevation model (DEM), and CROP are the primary impact factors affecting the synergy of WY–SC. This study may serve as a reference for examining the evolutionary mechanism underlying the trade-offs and synergies between ESs and provide a scientific basis for future ecological environmental protection and regional land management in the Taohe River Basin.

… (MGWR) and clustering analysis to spatially delineate … in heterogeneous enhancements of NPP across space and … 2.1–15 g C m⁻² to NPP, depending on the growth stage. No …

Nowadays, studies of ecosystem services (ESs) trade‐offs and synergies in karst areas are limited to a single region. In particular, the quantification of the nature and intensity of the relationships between ESs in continuous karst distribution areas (CKDAs) and non‐karst areas (NKAs), and a full understanding of the drivers behind this change remains a challenge. In this study, the province of Guizhou in China was used as study area and a framework was developed to analyze the spatial variation of trade‐offs and synergies between ESs. The framework utilized Spearman, geographical detector, and multi‐scale geographically weighted regression (MGWR) to establish connections between four key ESs: habitat quality (HQ), water yield (WY), soil conservation (SC), and net primary productivity (NPP). Through the examination of these relationships, this article has revealed the influence of the driving forces on this ecological process. The study indicated that: (1) During this period, ESs in study area increased steadily, with the average value of HQ always above 0.6, and WY, SC, and NPP increased by 2.32%, 6.07%, and 15.18%, respectively. (2) WY had a trade‐off relationship with other ESs, and SC, HQ, and NPP were synergistic with each other. (3) In the CKDA, topography and precipitation dominated the relationship between SC and other ESs. In contrast, in the NKA, ecological processes involving HQ were more prominent in human/natural interactions. Therefore, proposing ecological measures at different spatial scales to promote synergistic development between ESs is of great importance for ecological governance and sustainability of karst areas.

ABSTRACT Urbanization threatens smallholder agriculture, vital for global food security. This study uses Shanghai as a case, applying 2-m images and an improved Mobile-UNet with data augmentation to enhance the recognition of fragmented farmland. The model achieves 90.38% accuracy (mIoU=0.712, F1=0.831), outperforming logistic regression, random forest and gradient boosting by 6%–12% in the mIoU and 4%–9% in F1-score. Net primary productivity (NPP) data and 10 factors were analyzed through stepwise regression and geographically weighted regression (GWR). Stepwise regression explains 48% of the NPP variation, identifying potential evapotranspiration, evapotranspiration, land surface temperature, solar radiation and the digital elevation model as the 5 most important factors. GWR revealed that the strength and direction of relationships between factors and NPP vary significantly. The results informed hierarchical management maps and strategies for targeted resource allocation and farmland productivity policies, advancing sustainable urban agriculture through improved small-scale farmland regulation.

Vegetation is a crucial component of terrestrial ecology and plays a significant role in carbon sequestration. Monitoring changes in vegetation ecological quality has important guidance value for sustainable development. In this study, we investigated the spatial and temporal variation characteristics of Ecological Quality Index of Terrestrial Vegetation (EQI) in Anhui Province during the growing season from 2000 to 2020 using trend analysis, partial correlation analysis and bivariate spatial autocorrelation analysis. Based on the Multiscale Geographically Weighted Regression (MGWR), the spatial heterogeneity of the effects of average temperature, precipitation, elevation, slope, and human activity factors on EQI was explored. Our results showed an increasing trend in EQI during the growing season in Anhui Province from 2000 to 2020. The significantly increasing areas accounted for 43.49%, while the significantly decreasing areas accounted for 3.60%. EQI had a mostly positive correlation with precipitation and a negative correlation with average temperature (p < 0.1), showing a higher sensitivity to precipitation than to temperature. Additionally, EQI tended to increase initially and then decrease with increasing elevation and slope. Furthermore, our analysis revealed a significant negative spatial correlation between human activity intensity and EQI (p < 0.01). The bivariate global autocorrelation Moran index between EQI and human activity in 2000, 2005, 2010, 2015, and 2018 were −0.418, −0.427, −0.414, −0.487, and −0.470, respectively. We also found that the influencing factors explain 63–83% of the spatial variation of EQI, and the order of influence of factors on EQI is elevation > human activity > slope > average temperature > precipitation. MGWR results indicated that human activities and topographic factors had a stronger impact on EQI at the local scale, while climate factors tended to influence EQI at the global scale.

… We further separate the contributions of climatic factors into main and interactive effects (figure 3), and our results show main effects of climatic drivers especially for SWdif and TA are …

Temperature and water stress are important factors limiting the gross primary productivity (GPP) in terrestrial ecosystems, yet the extent of their influence across ecosystems remains uncertain. This study examines how surface air temperature, soil water availability (SWA) and vapor pressure deficit (VPD) influence ecosystem light use efficiency (LUE), a critical metric for assessing GPP, across different ecosystems and climatic zones at 80 flux tower sites based on in situ measurements and data assimilation products. Results indicate that LUE increases with temperature in spring, with higher correlation coefficients in colder regions (0.79–0.82) than in warmer regions (0.68–0.78). LUE reaches a plateau earlier in the season in warmer regions. LUE variations in summer are mainly driven by SWA, exhibiting a positive correlation indicative of a water‐limited regime. The relationship between the daily LUE and daytime temperature shows a clear seasonal hysteresis at many sites, with a higher LUE in spring than in fall under the same temperature, likely resulting from younger leaves being more efficient in photosynthesis. Drought stress influences LUE through SWA in all ranges of water availability; VPD variation under moderate conditions does not have a clear influence on LUE, but extremely high VPD (exceeding the threshold of 1.6 kPa, often observed during extreme drought‐heat events) causes a dramatic reduction of LUE. Our findings provide insight into how ecosystem productivities respond to climate variability and how they may change under the influence of more frequent and severe heat and drought events projected for the future.

… The net primary productivity (NPP) was used as an indicator to study the spatial and … climate variations (CV) and human activities (HA) to NPP changes across the QR, while their drivers …

Net primary productivity (NPP) can effectively represent the growth status of plants. The accurate estimation and spatiotemporal variation analysis of NPP is of great significance for understanding global climate change and carbon cycle. The Carnegie–Ames–Stanford Approach (CASA) model has been extensively utilized for estimating NPP by integrating remote sensing data, climate data, and land cover type data to achieve precise estimations of NPP. However, the traditional CASA model fails to account for the fact that crop types can change over time, leading to inaccurate estimations of NPP in the region. To address this issue, an adaptive temperature stress coefficient for the CASA model (tCASA) is proposed to estimate the NPP of the Central Plains Urban Agglomeration (CPUA) using Landsat time-series data spanning from 2002 to 2022. Based on the Theil-Sen Median Estimators, the Mann–Kendall test, and partial correlation analysis, this article delves into the impact of climate and human activities on NPP. The results indicate the following: the improved tCASA model exhibits a stronger linear correlation with the test dataset, achieving a reduction of 11.11 gC/m2 in the root mean square error. The NPP in CPUA demonstrates an increasing trend of 3.0683 gC/m2/year. Across the entire study area, 62.879% of the regions displayed an improvement in NPP, whereas 26.143% showed signs of degradation. Temperature and solar radiation demonstrate a strong positive correlation with NPP, with 73.421% and 72.839% of the region, respectively, positively linked. In contrast, precipitation shows a weaker correlation with NPP, with only 36.848% of the region indicating a positive relationship between precipitation and NPP.

… Early and delayed start of the photosynthetic period (SOP) directly affects the vegetation net primary productivity (NPP). However, the interrelationship between climate change, the SOP…

Elevation gradients significantly influence net primary productivity (NPP), but the relationship between elevation, climate variables, and vegetation productivity remains underexplored, …

… Changes in net primary productivity (NPP) due to global … that NPP exhibited stronger climate and soil nutrient responses, … PFE showed consistent responses to climate and soil nutrient …

Both net primary productivity (NPP) and vegetation phenology play essential roles in influencing the carbon sequestration of terrestrial ecosystems. However, the relationship between …

… of climate change (CC) and human activity (HA) to AD and the factors influencing the loss of winter wheat net primary … , were identified as the primary drivers of AD; spatiotemporal …

… driving mechanism of net primary productivity (NPP) in each terrestrial ecosystem of China under climate … and temperature, which are two key climate factors on the NPP, by correlation …

… (SIF), gross primary productivity (GPP), and net primary productivity (NPP) were increased in … driving mechanisms of temperature-phenology-SM interactions on ecosystem productivity. …

… the climate change and human afforestation. The water yield and net primary productivity … low water yield and reduction of net primary productivity may be associated with a strong El …

The frequency and severity of hot drought will increase in the future due to impact of climate change and human activities, threatening the sustainability of terrestrial ecosystems and human societies. Hot drought is a typical type of drought event, high vapor pressure deficit (VPD) and low soil moisture (SM) are its main characteristics of hot drought, with increasing water stress on vegetation and exacerbating hydrological drought and ecosystem risks. However, our understanding of the effects of high VPD and low SM on vegetation productivity is limited, because these two variables are strongly coupled and influenced by other climatic drivers. The southwestern United States experienced one of the most severe hot drought events on record in 2020. In this study, we used SM and gross primary productivity (GPP) datasets from Soil Moisture Active and Passive (SMAP), as well as VPD and other meteorological datasets from gridMET. We decoupled the effects of different meteorological factors on GPP at monthly and daily scales using partial correlation analysis, partial least squares regression, and binning methods. We found that SM anomalies contribute more to GPP anomalies than VPD anomalies at monthly and daily scales. Especially at the daily scale, as the decoupled SM anomalies increased, the GPP anomalies increased. However, there is no significant change in GPP anomalies as VPD increases. For all the vegetation types and arid zones, SM dominated the variation in GPP, followed by VPD or maximum temperature. At the flux tower scale, decoupled soil water content (SWC) also dominated changes in GPP, compared to VPD. In the next century, hot drought will occur frequently in dryland regions, where GPP is one of the highest uncertainties in terrestrial ecosystems. Our study has important implications for identifying the strong coupling of meteorological factors and their impact on vegetation under climate change.

… Net primary productivity (NPP) has been the subject of numerous studies, but the qualitative and comparative study on NPP that considers human activities, climate … , and driving factors …

in China over the past 30 years, but has recently experienced a succession of droughts caused by high precipitation variability, potentially threatening vegetation productivity in the region. Yet, the impact of precipitation anomalies on the vegetation primary productivity is poorly known. We used an asymmetry index (AI) to explore possible asymmetric productivity responses to precipitation anomalies in Southwest China from 2003 to 2018, using a precipitation dataset, combined with gross primary productivity (GPP), net primary productivity (NPP), and vegetation optical depth (VOD) products. Our results indicate that the vegetation primary productivity of Southwest China shows a negative asymmetry, suggesting that the increase of vegetation primary productivity during wet years exceeds the decrease during dry years. However, this negative asymmetry of vegetation primary productivity was shifted towards a positive asymmetry during the period of analysis, suggesting that the resistance of vegetation to drought, has increased with the rise in the occurrence of drought events. Among the different biomes, grassland vegetation primary productivity had the highest sensitivity to precipitation anomalies, indicating that grasslands are more flexible than other biomes and able to adjust primary productivity in response to precipitation anomalies. Furthermore, our results showed that the asymmetry of vegetation primary productivity was influenced by the effects of temperature, precipitation, solar radiation, and anthropogenic and topographic factors. These findings improve our understanding of the response of vegetation primary productivity to climate change over Southwest China.

… net primary productivity (NPP) in general, have dramatically changed in time and space since the 1980s, with climate … that climate and human activities are the common driving factors of …

… net primary productivity. An optimal parameter geographic detector model was used to scientifically identify the drivers … evapotranspiration were the primary drivers affecting the ESs. The …

Evidence for the multifaceted responses of terrestrial ecosystems has been shown by the weakening of CO2 fertilization‐induced and warming‐controlled productivity gains. The intricate relationship between vegetation productivity and various environmental controls still remains elusive spatially. Here several inherent preponderances make China a natural experimental setting to investigate the interaction and relative contributions of five drivers to gross primary productivity for the period from 1982 to 2018 (i.e., elevated atmospheric CO2 concentrations, climate change, nutrient availability, anthropogenic land use change, and soil moisture) by coupling multiple long‐term datasets. Despite a strikingly prominent enhancement of vegetation productivity in China, it exhibits similar saturation responses to the aforementioned environmental drivers (elevated CO2, climatic factors, and soil moisture). The CO2 fertilization‐dominated network explains the long‐term variations in vegetation productivity in humid regions, but its effect is clearly attenuated or even absent in arid and alpine environments controlled by climate and soil moisture. Divergence in interactions also provides distinct evidence that water availability plays an essential role in limiting the potential effects of climate change and elevated CO2 concentrations on vegetation productivity. Unprecedented industrialization and dramatic surface changes may have breached critical thresholds of terrestrial ecosystems under the diverse natural environment and thus forced a shift from a period dominated by CO2 fertilization to a period with nonlinear interactions. These findings suggest that future benefits in terrestrial ecosystems are likely to be counteracted by uncertainties in the complicated network, implying an increasing reliance on human societies to combat potential risks. Our results therefore highlight the need to account for the intricate interactions globally and thus incorporate them into mitigation and adaptation policies.

Intensified droughts have been weakening global vegetation productivity, yet how the sensitivity of vegetation productivity to drought changes over time is not well known. Here, using the simulated long‐term gross primary production (GPP) with an improved two‐leaf light use efficiency model and the Standardized Precipitation Evapotranspiration Index (SPEI), we studied the sensitivity of global vegetation productivity to drought, quantified by the corresponding time scale of SPEI with strongest drought impact on GPP, and analyzed the changes in the sensitivity over two time periods (1993–2005 and 2006–2018). Compared to the first period, droughts were more widespread and severer around the world in the second period, as evidenced by increased drought range (increased by 4.43%) and intensity (SPEI03 decreased by 103%). Globally, the area with significant correlation between GPP and SPEI increased by 25.53%, the impact intensity increased by 14.75%, and the drought sensitivity of GPP enhanced by 13.76%; the changing directions were pretty similar across various vegetation types, mostly showing an increasing trend. Moreover, the vegetation in regions with consistently decreasing moisture was affected by drought most strongly and experienced the greatest change in the sensitivity of GPP to drought (enhanced by 10.99%), indicating that the arid and semi‐arid ecosystems should be considered as a research priority in the future. Our results reveal strengthened drought sensitivity of global vegetation productivity in recent decades across various ecosystems and climate transition regions, which could improve our understanding on the behavior and fate of terrestrial ecosystems in the changing climate.

Climate change and large‐scale ecological restoration programs have profoundly influenced vegetation greening and gross primary productivity (GPP) in China's drylands. However, the specific pathways through which climatic factors and vegetation greening influence GPP remain poorly understood. This study examines the spatiotemporal changes in GPP across China's drylands from 2001 to 2020 and investigates the direct and indirect effects of climatic factors and leaf area index (LAI) on GPP. The results reveal that the overall improvement in vegetation cover has positively increased GPP in these regions. Although the direct effects of climatic factors on GPP are minimal, they exert a substantial indirect effect by regulating vegetation growth, highlighting that LAI is a key intermediary in mediating the effects of climatic factors on GPP. Furthermore, these complex interactions vary significantly along the aridity gradient. This study emphasizes the necessity of comprehensively considering the intricate interactions among multiple climate and vegetation factors.

Agricultural drought (AD) is the main environmental factor affecting vegetation productivity (VP) in the Yellow River Basin (YRB). In recent years, the nonlinear effects of AD on VP in the YRB have attracted much attention. However, it is still unclear whether fluctuating AD will have complex nonlinear effects on VP in the YRB, and there are scant previous studies at large scale on whether there is a threshold for nonlinear effects of AD on VP in the YRB. Therefore, this study used a newly developed agricultural drought index to explore nonlinear effects on VP revealing the nonlinear effects of AD on VP in the YRB. First, we developed a kernel temperature vegetation drought index (kTVDI) based on kernel normalized difference vegetation index (kNDVI) and land surface temperature data to study the spatiotemporal variation of AD in the YRB. Second, we used GPP data from solar-induced chlorophyll fluorescence inversion as an indicator to explore the spatiotemporal variation of VP in the YRB. Finally, we used several statistical indicators and a distributed lag nonlinear model (DLNM) to analyze the nonlinear effect of AD on VP in the YRB. The results showed that AD decreased significantly during 2000-2020, mainly in the southeast of the Loess Plateau, while GPP increased significantly in 80.93 % of the YRB. Meanwhile, moderate and severe AD stress limited VP growth, with the negative effects gradually decreasing, while mild AD had an increasingly positive promoting effect on VP. AD stress resulted in a VP decrease of 69.78 %, and severe AD stress resulted in a VP decrease of 65.52 %, mainly distributed in the northern Loess and Ordos Plateau. AD had significant nonlinear effects on VP. The effects of moderate and severe AD on the sustained nonlinear lag of vegetation were more obvious, and those of moderate and severe AD on the nonlinear lag of VP were the largest when the lag was approximately 1 month and 7 months. The effect of AD on the nonlinear hysteresis of VP in YRB was significantly different under different vegetation types, and forests were more able to withstand longer and more severe droughts than grasslands and croplands. The results of the study provide a theoretical basis for evaluating AD and analyzing the nonlinear impact of AD on VP. This will provide scientific basis for studying the mechanism of drought effect on vegetation in other regions.

… the climate changes offset the reduced vegetation productivity from rapid urbanization. The … vegetation productivity due to the lost vegetated land and degraded vegetation productivity. …

… Exploring the spatiotemporal changes of vegetation net primary productivity (NPP) and its … center model, wavelet analysis, coefficient of variation, Hurst index, correlation analysis, and …

The impact of atmospheric vapor pressure deficit (VPD) on plant photosynthesis has long been acknowledged, but large interactions with air temperature (T) and soil moisture (SM) still hinder a complete understanding of the influence of VPD on vegetation production across various climate zones. Here, we found a diverging response of productivity to VPD in the Northern Hemisphere by excluding interactive effects of VPD with T and SM. The interactions between VPD and T/SM not only offset the potential positive impact of warming on vegetation productivity but also amplifies the negative effect of soil drying. Notably, for high-latitude ecosystems, there occurs a pronounced shift in vegetation productivity’s response to VPD during the growing season when VPD surpasses a threshold of 3.5 to 4.0 hectopascals. These results yield previously unknown insights into the role of VPD in terrestrial ecosystems and enhance our comprehension of the terrestrial carbon cycle’s response to global warming.

… fast vegetation regains its pre-fire productivity levels … fire vegetation productivity recovery from 2004 to 2021 using gross primary productivity observations and related proxies at a spatial …

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

Resilience is a fundamental concept for vegetation health. The increasing drought frequency and severity may pose severe threat to vegetation resilience. However, it is still not clear how vegetation resilience is evolving in response to climate change in pivotal biographical zones. Here, we examined the resilience changes in terms of leaf area index (LAI, an indicator of canopy structure) and gross primary productivity (GPP, an indicator of carbon uptake) in responding to the Standardized Precipitation‐Evapotranspiration Index (SPEI) and vapor pressure deficit (VPD) over China's Loess Plateau and Qinling Mountains. Linking remote sensing variables and tree ring width allows the upscaling of plot‐based vegetation growth information. We further explored potential explanatory factors associated with the heterogeneous spatial distributions of resilience changes. Results revealed that the resilience of GPP weakened more than LAI in response to drought, suggesting that compared to LAI, productivity requires more time to recover to the pre‐drought levels. Regionally, the change of vegetation resilience on the Loess Plateau and in high‐altitude areas was highly susceptible to SPEI and VPD, respectively. The observed spatial heterogeneity in resilience changes was mainly attributed by climate zone, water deficit, and their interactions. Our findings provide direct and empirical evidence that the vegetation in the Loess Plateau and Qinling Mountains is gradually losing resilience. The results indicate that sustained ecosystem water deficit and atmospheric dryness will continue to threaten vegetation survival and terrestrial ecosystem service.

&lt;p&gt;Biological and ecological processes regulating the ecosystem carbon cycle exhibit varying sensitivities to temperature fluctuations during the day and night. Consequently, the diurnal temperature range (DTR)—the difference between daily maximum and minimum temperatures—plays an important role in modulating carbon assimilation and consumption in plants. Over recent decades, daytime warming has outpaced nighttime warming over land, leading to a widening of the DTR, which is expected to impact plant productivity. However, how the recent DTR changes have influenced vegetation productivity across various climate zones remains unclear. Using remote sensing data and flux tower measurements from 2002 to 2021, we found divergent impacts of increased DTR on vegetation productivity in the extratropical Northern Hemisphere. In humid zones, summer DTR increases have promoted net primary production (NPP), while the opposite effect is found in arid zones. This contrast can largely be explained by the larger impact of accelerated daytime warming on increased vapor pressure deficit in arid zones, which consequently inhibits NPP. Our findings underscore the non-negligible impacts of recent DTR changes on vegetation productivity, emphasizing the need to consider sub-diurnal variations in assessments of climate change impacts.&lt;/p&gt;

ABSTRACT Net ecosystem productivity (NEP) quantifies magnitude of the terrestrial vegetation carbon sinks. Drought is one of the most important stressors affecting vegetation NEP. At present, the spatiotemporal dynamics of vegetation NEP in drought-prone of Northwest China (NWC) lack discussion under different climatic zones and land cover types, and the response of vegetation NEP to drought remains unclear. Hence, we estimated the vegetation NEP in NWC using ground and remote sensing data and quantified the spatiotemporal differentiation of NEP under different climatic zones and land cover types. The drought fluorescence monitoring index (DFMI) was developed to examine the relationship between vegetation NEP and drought response based on the solar-induced chlorophyll fluorescence (SIF) data. Our results suggested that vegetation carbon sinks increased significantly at 7.09 g C m−2 yr−1 in NWC during 2000–2019, mainly in northern Shaanxi, eastern and southern Gansu, and southern Ningxia. NEP showed increasing trends under different climatic zones and land cover types, but there were differences in carbon sink capacity. The strongest carbon sink capacity was in humid regions and forests, while the weakest was in arid regions and grasslands. The vegetation carbon sinks showed a non-linear relationship with the drought degree reflecting multiple trend differences, especially in forests and grasslands. The response to drought was faster and more significant in semi-arid and semi-humid transition zones and extreme humid regions when vegetation carbon sinks decreased. DFMI was a good indicator to monitor drought conditions in NWC. NEP and DFMI were an 8–20-month periodic positive correlation and showed a high correlation with high–high and low–low clustering spatially. Drought significantly weakened vegetation carbon sinks in NWC. This study emphasizes the demand to rapidly identify climatic conditions that lead to decrease significantly in vegetation carbon sinks and to formulate adaptation strategies aimed at reducing drought risk under global warming.

Increasingly drastic global change is expected to cause hydroclimatic changes, which will influence vegetation productivity and pose a threat to the terrestrial carbon sink. Asymmetry represents an imbalance between vegetation growth and loss of growth during dry and wet periods, respectively. However, the mechanisms of asymmetric plant responses to hydrological changes remain poorly understood. Here, we examined the spatiotemporal patterns of asymmetric responses of vegetation productivity across terrestrial ecosystems in China. We analyzed several observational and satellite-based datasets of plant productivity and several reanalyzed datasets of hydroclimatic variables from 2001 to 2020, and used a random forest model to assess the importance of hydroclimatic variables for these responses. Our results showed that the productivity of >50 % of China's vegetated areas showed a more positive asymmetry (2.3 ± 9.4 %) over the study period, which were distributed broadly in northwest China (mainly grasslands and sparse vegetation ecosystems). Negative asymmetries were most common in forest ecosystems in northeast China. We demonstrated that one-third of vegetated areas tended to exhibit significant changes in asymmetry during 2001-2020. The trend towards stronger positive asymmetry (0.95 % yr-1) was higher than that towards stronger negative asymmetry (-0.55 % yr-1), which is beneficial for the carbon sink. We further showed that in China, soil moisture was a more important driver of spatiotemporal changes in asymmetric productivity than precipitation. We identified thresholds of surface soil moisture (20-30 %, volume water content) and root-zone soil moisture (200-350 mm, equivalent water height) that were associated with changes in asymmetry. Our findings highlight the necessity of considering the dynamic responses of vegetation to hydrological factors in order to fully understand the physiological growth processes of plants and avoid the possible loss of productivity due to future climate change.

ABSTRACT The novel coronavirus 2019 (COVID-19)-imposed restrictions in 2020 and 2021 led to a notable reduction in human activity, providing an opportunity to study the impact of human activity on global vegetation productivity. The impacts on vegetation productivity are of particular interest, as vegetation carbon sinks serve as one of the main pathways for carbon neutrality. This study investigated the impacts of the COVID-19 pandemic restrictions on global vegetation productivity in 2020 and 2021 by leveraging remotely sensed big data and model data. This study revealed reduced atmospheric emissions and increased radiation reaching the surface in these two years. Compared to the time period from 2017 to 2019, global vegetation productivity increased by 1.95% and 1.15% in 2020 and 2021, respectively, with a majority of countries hit by the COVID-19 pandemic showing enhanced vegetation productivity. This study conclude that a sudden reduction in human activities due to COVID-19 restrictions plays a positive role in global vegetation productivity and carbon neutrality. The widely implemented COVID-19 control measures at the global scale allow scholars to observe the responding mechanism of vegetation productivity, greatly benefiting the rethinking of existing sustainable development strategies.

Ongoing changes in snow cover significantly affect vegetation productivity, but the actual effect of snow cover remains unclear due to a poor understanding of its lagged effect. Here, we used multisource datasets to investigate the lagged effect of snow cover on vegetation productivity in Northern Hemisphere ( > 40°N) ecosystems from 2000 to 2018. We found a widespread lagged effect of snow cover ( > 40%, P < 0.05) on growing season vegetation productivity (mean ~73-day lag). The effect of snow cover on vegetation productivity was underestimated by over 10% of the areas without considering regional lagged time differences. A longer lagged effect generally occurred in warm and humid areas, and areas with increased lagged time (66%) were greater than those with decreased trends. Moreover, changes in lagged effect were strongly driven by climate factors, followed by soil and topography factors. These findings emphasize the need to consider lagged time differences of snow cover when investigating snow-vegetation productivity interactions.

Net Primary Productivity (NPP) is a vital ecological indicator used to monitor land productivity and the health of ecosystems, particularly in climate-sensitive areas like the Eastern Sahel. However, the spatial heterogeneity in the relationships between NPP and environmental factors complicates accurate predictions. This research aimed to evaluate the effectiveness of geographically weighted statistical and machine learning models in predicting NPP, while considering spatial non-stationarity and non-linear interactions. The study used 939 spatial observations of the NPP in conjunction with four environmental predictors: rainfall, temperature, soil moisture, and elevation, spanning Niger, Chad, and Sudan. Initially, a global Ordinary Least Squares (OLS) model was used as a reference point. Subsequently, three geographically weighted models, Geographically Weighted Regression (GWR), Geographically Weighted Random Forest (GWRF), and Geographically Weighted Neural Network (GWNN) were executed to account for spatial variability and non-linear effects. The performance of the models was assessed using R², MSE, RMSE, MAE, and spatial residual diagnostics. All geographically weighted models outperformed the global OLS baseline in terms of both predictive accuracy and spatial sensitivity. GWNN achieved the highest performance (R2 = 0.9360; RMSE = 0.0333), followed closely by GWRF (R2 = 0.9308) and GWR (R2 = 0.9207), compared to OLS (R2 = 0.8354). The residual spatial autocorrelation was completely resolved in GWNN and GWRF. Rainfall was consistently the most significant predictor, while the effects of other variables, such as elevation and temperature, varied between different spatial contexts. The findings of this research emphasise the value of combining spatial weighting with machine learning methodologies to model ecological productivity in heterogeneous landscapes. The GWNN model, in particular, stands out as a powerful tool for improving NPP predictions in regions sensitive to climate change.

… However, a mechanistic, multi-scale understanding of the drivers … Geographical Detector (OPGD), Multiscale Geographically Weighted Regression (MGWR), and Partial Least Squares …

… This multi-scale approach advances regional carbon neutrality planning … geographical features and land use change driven by human activities influence the spatial distribution of NPP. …

… NPP changes in the perspectives of land use types and provinces. Finally, we used geographically weighted regression (GWR) model to analyze effects of different factors on NPP. The …

… learning model, using monthly NPP imagery data from the western … It employs the Getis-Ord Gi* statistic to capture NPP's spatial … Collectively, this model provides a high-precision NPP …

… dynamics and climate change and human activities is important for the government to … (NPP) as an indicator, based on multitemporal land use data, to separate anthropogenic and …

Understanding the drivers of changes in vegetation net primary productivity (NPP) is critical for comprehending ecosystem dynamics and their ability to respond to environmental shifts. However, the complexity and nonlinear variations of NPP across the Tibetan Plateau, along with spatial and temporal inconsistencies, present significant analytical challenges. This study leverages the Google Earth Engine (GEE) platform and applies non-parametric trend analysis methods, such as the Sen slope estimator, Mann-Kendall test, coefficient of variation, and Hurst exponent, to investigate NPP trends from 2001 to 2021. The Optimal Parameters-based Geographical Detector (OPGD) model was employed to assess the combined effects of natural factors and human activities on NPP’s spatial distribution and variability, identifying key drivers and their optimal ranges for promoting NPP growth. Results revealed nonlinear fluctuations in NPP during the study period, ranging from 184.06 to 208.53 gC m-2.a-1, with an average annual growth rate of 1.16 gC m-2.a-1. Significant spatial differences were observed, with higher NPP in the grasslands and forests of the southeast, while lower productivity was found in the alpine deserts of the northwest. Over 55% of the study area showed an increasing trend in NPP, with 28.14% experiencing significant growth (p < 0.05). The study further indicated that natural factors such as elevation, solar radiation, and mean annual temperature were major determinants of NPP fluctuations, while human activities (e.g., distance, population density, and land use) also played a crucial role in shaping NPP patterns. The significant interaction between natural factors and human activities demonstrates synergistic enhancement and non-linear effects, highlighting the complexity of multi-factor drivers influencing NPP changes. The key promoting factors and their optimal ranges identified provide a foundation for understanding the impact of natural and human activities on NPP variation, offering scientific support for ecosystem management and sustainable development on the Tibetan Plateau.

Net primary productivity (NPP) plays a vital role in the globe carbon cycle. Quantitative assessment of the effects of climate changes and human activities on net primary productivity dynamics is vital for understanding the driving mechanisms of vegetation change and sustainable development of ecosystems. This study investigates the contributions of climatic factors and human activities to vegetation productivity changes in China from 2000 to 2020 based on the residual trend analysis (RESTREND) method. The results showed that the annual average net primary productivity in China was 325.11 g C/m2/year from 2000 to 2020 and net primary productivity showed a significantly increasing trend (p<0.05) at a rate of 2.32 g C/m2/year. Net primary productivity increased significantly (p<0.05) across 40.90% of China over the study period, while only 1.79% showed a significantly declining trend (p<0.05). The contributions of climatic factors and human activities to net primary productivity increase were 1.169 g C/m2/year and 1.142 g C/m2/year, respectively. Climate factors contributed positively mainly in Sichuan Basin, the Loess Plateau, the Mongolian Plateau, and Northeast China Plain. Positive contributions of human activities to net primary productivity mainly occurred in the Loess Plateau, Central China, and the Greater Khingan Mountains. The effects of climatic factors and human activities on net primary productivity changes varied among sub-regions. In Tropical Monsoon Climate Region and Subtropical Monsoon Climate Region, human activities had greater impacts on net primary productivity increase than climate factors, while climate factors were the dominant factor for net primary productivity recovery in other sub-regions. In addition, during 2000–2020, net primary productivity was dominated by both climate factors and human activities in 49.84% of China, while areas dominated solely by climate factors and human activities accounted for 13.67% and 10.92%, respectively. Compared to changed land cover types, the total net primary productivity as well as the increase of total net primary productivity in China was mostly contributed by unchanged land cover types, which contributed more than 90%.

… human activity reduces NPP. In areas with higher and more concentrated human activity, it suppresses NPP, … The threshold of human activity intensity affecting NPP varies across space …

Net primary productivity (NPP) is a critical indicator for evaluating the carbon sequestration potential of an ecosystem and regional sustainable development, as its spatiotemporal dynamics are jointly influenced by natural and anthropogenic factors. This study investigated the Sangong River Basin, an inland watershed located in northwestern China. By employing the Carnegie–Ames–Stanford Approach (CASA) model and the Geodetector method, integrated with remote sensing data and field surveys, we systematically analyzed the spatiotemporal evolution and driving mechanisms of NPP from 1990 to 2020. Our results reveal an average annual basin-wide NPP increase of 2.33 g C·m−2·a−1, with plains experiencing significantly greater increases (2.86 g C·m−2·a−1) than mountains (1.71 g C·m−2·a−1). Land use intensity (LUI) explained 31.44% of the NPP variability in the plains, whereas climatic factors, particularly temperature (71.27% contribution rate), primarily governed the NPP dynamics in mountains. Soil properties exhibited strong associations with NPP. Specifically, a 1 g·kg−1 increase in soil organic content elevated NPP by 99.04 g C·m−2·a−1, while a comparable rise in soil salinity reduced NPP by 123.59 g C·m−2·a−1. These findings offer spatially explicit guidance for ecological restoration and carbon management in arid inland basins, underscoring the need for a strategic equilibrium between agricultural intensification and ecosystem conservation to advance carbon neutrality objectives.

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region’s net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m−2·yr−1 during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m−2·yr−1, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m−2·yr−1, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance.

… (NPP) and its driving mechanisms from 2000 to 2022 using MODIS NPP data, climatic factors (… The findings show that (1) the annual mean NPP is 597.5 gC/m² in the Qinling Mountains, …

… net primary productivity (NPP) is mainly driven by biotic (… This study analyzed the NPP distribution pattern in the Yangtze … the impact of climate and human activity on the NPP along the …

Under the combined impact of climatic, socioeconomic, and environmental factors, the vegetation NPP change process and its responses to drive factors in the sub-regions of Mainland China are not clear. This study analyzes the changing pattern of vegetation NPP in China from 2000 to 2022 from the perspective of zoning and clarifies its response mechanism to climate-human interaction based on the gravity center model, third-order partial correlation coefficient and geographical detector. The results showed that: (1) There was an overall decreasing trend of vegetation NPP in China from the southeast to the northwest; (2) The vegetation NPP gravity center in Northeast, Northwest, and North China migrated southwards, while that of Southwest, Central South, and East China showed northward migration.;(3) Human activities played a dominant role in zones with increasing vegetation NPP from 2000 to 2010, while climate change greatly contributed to the increase in vegetation NPP during 2011–2022; (4) Human activities, such as deforestation and overgrazing, in Northeast and North China should be reduced to prevent vegetation ecosystem degradation, and the negative impact of human activities should be reduced to maintain the growth of vegetation NPP. This study was conducted to support decision-making for the precise restoration of ecosystems.

To assess the carbon balance of terrestrial ecosystems, it is crucial to consider the net primary productivity (NPP) of vegetation. Understanding the response of NPP in Xinjiang’s vegetation to climate factors and human activities is essential for ecosystem management, the Belt and Road Initiative, and achieving carbon neutrality goals. Based on the CASA model, this study uses meteorological data, DEM data, and land cover data, employing trend analysis and partial derivative analysis methods to investigate the temporal trends and spatial distribution of NPP in Xinjiang from 2000 to 2020. Additionally, it quantifies the contributions of climate factors and human activities to NPP fluctuations. The key findings are: (1) The average annual NPP is 101.52 gC/m2, with an upward trend, showing an overall growth rate of 0.447 gC/m2/yr. Spatially, NPP is higher in northern Xinjiang than in the south, and in mountainous areas compared to basins. (2) Over 21 years, climate factors contributed an average of 1.054 gC/m2/yr, while human activities contributed 0.239 gC/m2/yr to NPP changes. Among climate factors, temperature, precipitation, and sunshine duration contributed 0.003, 0.169, and 0.588 gC/m2/yr, respectively, all showing positive effects on NPP. (3) Forests have the highest average NPP at 443.96 gC/m2, with an annual growth rate of 2.69 gC/m2/yr. When forest is converted to cropland, the net loss in NPP is −1.94 gC/m2, and the loss is even greater in conversion to grassland, reaching −17.33 gC/m2. (4) The changes in NPP are driven by both climate factors and human activities. NPP increased in 77.25% of the area, while it decreased in 22.69%. Climate factors have a greater positive impact than human activities.

… with anthropogenic disturbance. However, comparative studies on how the driving factors of natural and human activities … Therefore, net primary productivity (NPP) was chosen as an …

Formulating ecological restoration strategies requires accurately quantifying how climate and anthropogenic factors influence net primary production (NPP). A Carnegie-Ames-Stanford approach (CASA) model was applied to estimate China’s terrestrial NPP from 2001 to 2020. We adopted a random forest (RF) method to identify the main driving forces for NPP change in China. Total NPP in China increased noticeably with a 24.91 Tg C/yr rate, as shown in our results. The significantly increased NPP was mainly attributed to human activities (64.29 ± 0.17%), chiefly due to human management and ecological projects (afforestation or other) fostered vegetation growth. The primary drivers of NPP variation varied in different geographic regions. Climate dominated the NPP dynamic in north China (52.38 ± 0.91%), where the main factor that restricted the increase of NPP was precipitation. Human activities strongly impacted the NPP variation in the remaining regions. Human management measures increased NPP in northwest and southwest China. In the northeast, east, and south-central China, the NPP change resulted from land use change, primarily grassland, cropland, and forest change. Collectively, our study expands the understanding of the driving forces of NPP change, informing different strategies for achieving ecological restoration and carbon neutrality.

Net primary productivity (NPP) can indirectly reflect vegetation’s capacity for CO2 fixation, but its spatiotemporal dynamics are subject to alterations to some extent due to the influences of climate change and human activities. In this study, NPP is used as an indicator to investigate vegetarian carbon ability changes in the vital ecosystems of the Yangtze River Basin (YRB) in China. We also explored the NPP responses to climate change and human activities. We conducted a comprehensive analysis of the temporal dynamics and spatial variations in NPP within the YRB ecosystems from 2003 to 2020. Furthermore, we employed residual analysis to quantitatively assess the contributions of climate factors and human activities to NPP changes. The research findings are as follows: (1) Over the 18-year period, the average NPP within the basin amounted to 543.95 gC/m2, displaying a noticeable fluctuating upward trend with a growth rate of approximately 3.1 gC/m2; (2) The areas exhibiting an increasing trend in NPP account for 82.55% of the total study area. Regions with relatively high stability in the basin covered 62.36% of the total area, while areas with low stability accounted for 2.22%, mainly situated in the Hengduan Mountains of the western Sichuan Plateau; (3) NPP improvement was jointly driven by human activities and climate change, with human activities contributing more significantly to NPP growth. Specifically, the contributions were 65.39% in total, with human activities contributing 59.28% and climate change contributing 40.01%. This study provides an objective assessment of the contributions of human activities and climate change to vegetation productivity, offering crucial insights for future ecosystem development and environmental planning.

Central Asia is one of the most sensitive regions to climate changes in the world and the grassland degradation of this region has attracted considerable concern. Quantifying the driving force of grassland degradation is important for understanding the effects of climate variation and human activities on grassland. In this study, net primary productivity (NPP) was selected as an indicator to quantitatively evaluate the relative role of climate variation and human activities in Central Asia from 2000 to 2020. This study used the global NPP product MOD17A3 as actual NPP and estimated the potential NPP using the Thornthwaite memorial model. The potential NPP and the difference between the potential NPP and actual NPP were used to represent the influence of climate variation and human activities. The grassland degradation or restoration can be demonstrated by the slope of actual NPP (SA). A positive slope value (SA) suggested that restoration occurs, whereas a negative slope value suggested that degradation occurred. The results showed that 23.08% of the total grassland area experienced grassland degradation, whereas 2.51% of the whole grassland underwent grassland restoration. Furthermore, 53.8% of the degraded grassland areas were influenced by climate variation, and 14.5% were caused by human activities. By contrast, the relative roles of climate variation and human activities in grassland restoration were 25% and 47.9%, respectively. The NPP variation also could be calculated by assessing the effects of these factors and the results showed that 55.7% of the NPP decrease was caused by climate variation, whereas 9.6% was a result of human activities. On the contrary, climate variation and human activities resulted in 19.8% and 37.3% of grassland restoration, respectively. Therefore, climate variation was the dominant factor of grassland degradation, and human activities were the main driver of grassland restoration in Central Asia.

ABSTRACT Vegetation net primary productivity (NPP) plays a crucial role in assessing the quality and function of terrestrial ecosystems. The Qilian Mountains (QLM) are an important ecological barrier and water conservation area in northwest China. However, the driving factors of the NPP change in the greening (NPP increased) area and browning (NPP decreased) area of QLM remain unclear. This study analyzes the spatiotemporal dynamics and driving factors of NPP in QLM over the past two decades by utilizing hydrometeorological data and human activity (HA) data. Employing spatial and trend analyses to explore the variation of NPP. Additionally, the gravity model was introduced to track the migration of NPP's gravity center, and the Geodetector model was employed to identify the driving factors and their interactive impacts on NPP change. Finally, the Hurst index was used to predict the persistence of the changing trend. Results reveal a fluctuating increasing NPP trend (2.38 gC m–2 a–1) in QLM from 2000 to 2020, with cultivated vegetation and broad-leaved forests showing greater increases. Approximately 75.37% of QLM pixels display increased NPP trends, primarily located in the southeastern regions. The NPP gravity center shifted northwestward by 18.24 km. Spatially, high NPP values cluster concentrated in the southeast, while low values cluster concentrated in the northwest. In the greening area, precipitation, vapor pressure deficit, and evapotranspiration dominate NPP changes, contributing 46.1%, 31.5%, and 25.0%, respectively. In the browning area, soil moisture, HA, and precipitation were the primary factors driving NPP change with contributions of 8.4%, 7.6%, and 6.6%, respectively. The results of the Geodetector model indicated that the explanatory power of a single factor was nonlinearly enhanced when it interacted with other factors. The Hurst index suggests that the NPP change was not persistent, showing clear reverse persistent characteristics, which implies uncertainty of the vegetation change in QLM. These findings reveal nonlinear responses of NPP to climate change and human activities in the context of global warming, providing insights for QLM's ecological protection and sustainable development.

Net Primary Productivity (NPP) is a critical metric for assessing terrestrial carbon sequestration and ecosystem health. While advancements in NPP modeling have enabled estimation at various scales, hidden anomalies within NPP time series necessitate further investigation to understand the driving forces. This study focuses on Shandong Province, China, generating a high-resolution (250 m) monthly NPP product for 2000–2019 using the Carnegie–Ames–Stanford Approach (CASA) model, integrated with satellite remote sensing and ground observations. We employed the Seasonal Mann–Kendall (SMK) Test and the Breaks For Additive Season and Trend (BFAST) algorithm to differentiate between gradual declines and abrupt losses, respectively. Beyond analyzing land use and land cover (LULC) transitions, we utilized Random Forest models to elucidate the influence of environmental factors on NPP changes. The findings revealed a significant overall increase in annual NPP across the study area, with a moderate average of 503.45 gC/(m2·a) during 2000–2019. Although 69.67% of the total area displayed a substantial monotonic increase, 3.89% of the area experienced abrupt NPP losses, and 8.43% exhibited gradual declines. Our analysis identified LULC transitions, primarily driven by urban expansion, as being responsible for 55% of the abrupt loss areas and 33% of the gradual decline areas. Random Forest models effectively explained the remaining areas, revealing that the magnitude of abrupt losses and the intensity of gradual declines were driven by a complex interplay of factors. These factors varied across vegetation types and change types, with explanatory variables related to vegetation status and climatic factors—particularly precipitation—having the most prominent influence on NPP changes. The study suggests that intensified land use and extreme climatic events have led to NPP diminishment in Shandong Province. Nevertheless, the prominent positive vegetation growth trends observed in some areas highlight the potential for NPP enhancement and carbon sequestration through targeted management strategies.

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. Using the Mann–Kendall methods, Pearson correlation, and the Geodetector, this study investigated the spatiotemporal variation and driving factors of GPP from 2000 to 2020. The results showed that (1) in terms of spatial distribution, GPP showed a trend of “low-high-low” regions, with low values for grassland and arable land and a high value for forest land. The growth trend is fast in forest areas, while the growth trend is not obvious in cultivated areas. The regions with significant growth accounted for 68.73% of the whole region. (2) The whole region shows a growth rate of 2.07 g C∙m−2 yr−1, showing obvious seasonality, with a slow growth trend in spring and autumn and a fast growth trend in summer. (3) The driving factors of GPP spatial differentiation in the Beijing-Tianjin-Hebei region were land surface temperature, land use type, and nighttime light data, while precipitation and downward surface shortwave radiation show no strong explanatory power for the spatial differentiation of GPP, which means that these two factors have less driving force on the spatial differentiation of GPP. The interaction of LUCC with the other factors presents two-factor enhancement, while the LST interaction with the other three factors presents non-linear enhancement. This study could provide a theoretical basis for the sustainable development of the Beijing-Tianjin-Hebei Region.

The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C·m2·yr−1) over the past 40 years, with a continuously increasing trend of 2.38 (g C·m2·yr−1). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change.

In this study, we determined whether changes in vegetation net primary productivity (NPP) can be used to characterize the quality of terrestrial ecosystems, which is critical for global change and carbon balance. We first explored the spatial correlation of NPP and its impact on vegetation restoration. MOD17A3 remote sensing products were used to analyze the temporal and spatial changes in NPP on the Loess Plateau (LP) over the last two decades (2000–2020). The resulting spatial autocorrelation indices identified cold and hot spots in the spatial clustering patterns. The effects of climate change and human activities on the anomalous clustering of NPP were assessed using Pearson correlation analysis and multi-temporal land use land cover data. The results indicate that i) Temporally, from 2000 to 2020, the NPP of the LP increased significantly by 6.88 g C m − 2 y r − 1 and so did the proportion of revegetated land area >400 g C m − 2 y r − 1 from 4% to 37%. Spatially, NPP showed an increasing trend from northwest to southeast. ii) The vegetation NPP on the LP showed a strong positive global spatial autocorrelation (p< 0.01). The hot and cold regions were polarized; the cold spots were clustered in the northwest, while the hot spots in the south and east. The spatial clustering patterns were dominated by high-high (HH) and low-low (LL) clusters. Abnormal patterns existed mainly in the transition areas between HH and LL clusters and insignificant regions, which were jointly affected by human activities and climate change. iii) Precipitation was the dominant climatic factor (86%) affecting the NPP variation in the LP, with the annual minimum precipitation showing a significantly positive relationship with the interannual variability in NPP, while the maximum precipitations greatly influenced the variation in local spatial anomaly patterns. This suggests that climatic extremes affect vegetation. Our study helps to facilitate green ecological management and high-quality development in the LP.

… of NPP growth or degradation of vegetation is unreliable. Therefore, this study examines spatial and temporal variations in NPP … past and future spatiotemporal patterns in NPP and used …

… ) model to estimate monthly NPP from 2017 to … a MODIS-derived product widely used for estimating NPP). Subsequently, we applied STL decomposition to detect seasonal trends in NPP…

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen’s tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate.

基于MODIS的大别山区NPP及NDVI时空动态及相关性研究 … 基于MODIS的大别山区NPP及NDVI时空动态及相关性研究 … 基于MODIS的大别山区NPP及NDVI时空动态 及相关性研究[J]. 信阳…

This study quantitatively evaluates the effects of human activities (HAs) and climate change (CC) on the terrestrial ecosystem carbon cycle, providing a scientific basis for ecosystem management and the formulation of sustainable development policies in urban agglomerations located in arid and ecotone regions. Using the LanXi urban agglomeration in China as a case study, we simulated the spatiotemporal variation of vegetation net primary productivity (NPP) from 2000 to 2023 based on MODIS remote sensing data and the CASA model. Trend analysis and the Hurst index were employed to identify the dynamic trends and persistence of NPP. Furthermore, the Geographical Detector model with optimized parameters, along with nonlinear residual analysis, was employed to investigate the driving mechanisms and relative contributions of HAs and CC to NPP variation. The results indicate that NPP in the LanXi urban agglomeration exhibited a fluctuating upward trend, with an average annual increase of 4.26 gC/m2 per year. Spatially, this trend followed a pattern of “higher in the center, lower in the east and west,” with more than 95% of the region showing an increase in NPP. Precipitation, mean annual temperature, evapotranspiration, and land use types were identified as the primary driving factors of NPP change. The interaction among these factors demonstrated a stronger explanatory power through factor coupling. Compared with linear residual analysis, the nonlinear model showed clear advantages, indicating that vegetation NPP in the LanXi urban agglomeration was jointly influenced by HAs and CC. These findings can further act as a basis for resource and environmental research in similar ecotone regions globally, such as Central Asia, the Mediterranean Basin, the southwestern United States, and North Africa.

… Utilizing a long-term NPP remote sensing inversion dataset, this study systematically uncovers the spatiotemporal evolution patterns of vegetation NPP in Asia through historical trend …

Global climate change is rooted in the imbalance between carbon sources and sinks, and net-zero greenhouse gas emissions should focus not only on the source-side drivers but also on the sink-side influencing factors. Taking the county-level administrative districts in China as the sample, this study uses machine learning models to fit the relationship between socioeconomic development (SED) and net primary productivity (NPP) of terrestrial ecosystems. Moreover, it identifies key influencing factors and their effects based on the SHapley Additive exPlanations (SHAP) algorithm. The results show that the districts with low terrestrial NPP show the characteristics of agglomeration distribution. The eight key factors, in order, are as follows: agricultural development level, latitude, population size, longitude, animal husbandry development level, economic scale, time trend and industrialization level. In this study, via SHAP interaction plots, we found that the effects of population, economic growth, and industrialization on terrestrial NPP are regionally heterogeneous; via cluster analysis, we found the stage characteristics of the mode of SED affecting terrestrial NPP. Therefore, the conservation of terrestrial NPP needs to be combined with the stage changes of SED, as well as inter-regional differences, to develop a regionally coordinated and time-coherent ecological carbon sink conservation plan.

Human interventions, such as farmland management, have long been considered crucial for soil carbon sequestration, but little is known about the exact impact of these interventions on the net carbon flux, represented by net ecosystem productivity (NEP). Here, using multiple long-term, large-scale data and statistical data, we reveal that 75.54% of farmland NEP in China experiences an increase, with northern regions showing the greatest potential for future farmland carbon sequestration. This growth is primarily attributed to the role of farmland management, especially the enhancement of no-tillage, land consolidation and multiple cropping level (17.02%, major grain-producing areas in 2020). Notably, the current unreasonable practices of mechanized straw returning and irrigation have a negative impact on farmland NEP. Our results show that it is imperative to acknowledge the crucial role of human interventions on farmland NEP to strike a balance between food security and farmland carbon sequestration. Farmland ecosystems are influenced by human interventions. Here, the authors assess the relationship of farmland management to net ecosystem productivity of farmland, finding a positive impact of 17.02% in major grain-producing areas of China.

Widespread land development, deforestation, and wetland degradation have disrupted the physical integrity and functional capacity of ecosystems, leading to a reduction in ecosystem service values (ESV). However, comprehensive research addressing ESV interactions that represent various ecosystem services from multifaceted angles is limited. Moreover, the relative significance and spatiotemporal diversity of natural and socio-economic variables influencing ESV demand further investigation. This study conducts both quantitative and qualitative assessments of the spatiotemporal dynamics and interrelationships of ESV in the Tibet autonomous region from 2000 to 2020. Geographical detector and geographically weighted regression models are applied to ascertain the relative importance and spatial heterogeneity of diverse ESV determinants. The findings reveal the following key insights: (1) Barren lands experienced the most substantial expansion from 2000 to 2020, indicating an exacerbation of desertification in the Tibet autonomous region. (2) Over the two decades, ESV exhibited an overall upward trajectory, with regulation of water flows, water bodies, and forests making the most significant contributions to ESV and its growth. (3) The quantitative and qualitative assessment of ESV interrelations has identified the number of trade-offs and synergies, along with spatial occurrences, offering a detailed foundation for the scientific management of ecosystems. Specifically, quantitative results portray ESV correlations as positive or negative, qualitative spatial mapping elucidates intricate local interactions among ESV. (4) The primary driver of ESV in the Tibet autonomous region is NDVI (0.072), with elevation following closely behind, underscoring the predominant influence of natural factors relative to socio-economic variables. This research serves as a scientific underpinning for the development of ecological conservation policies and the execution of ecological restoration initiatives.

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water‑carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

Sensitivity of ecosystem productivity to climate variability is a critical component of ecosystem resilience to climate change. Variation in ecosystem sensitivity is influenced by many variables. Here we investigate the effect of bedrock lithology and weathering products on the sensitivity of ecosystem productivity to variation in climate water deficit using Bayesian statistical models. Two thirds of terrestrial ecosystems exhibit negative sensitivity, where productivity decreases with increased climate water deficit, while the other third exhibit positive sensitivity. Variation in ecosystem sensitivity is significantly affected by regolith porosity and permeability and regolith and soil thickness, indicating that lithology, through its control on water holding capacity, exerts important controls on ecosystem sensitivity. After accounting for effects of these four variables, significant differences in sensitivity remain among ecosystems on different rock types, indicating the complexity of bedrock effects. Our analysis suggests that regolith affects ecosystem sensitivity to climate change worldwide and thus their resilience.

… function in the functional space F including the set of all possible classification and regression trees. The objective function to be optimized is then given using real values y i as follows: …

… net primary productivity (NPP) and ecosystem … and spatial pattern of carbon sink in China from 2000 to 2018 were then revealed, and the carbon sink capacity of various ecosystems …

… However, the currently available moderate-spatial-resolution GPP algorithms and products … , simple linear regression and random forest regression, to capture the high spatial resolution …

The insurance hypothesis posits that more diverse communities are more stable through time. Here, the authors show that plant biodiversity reduces the spatial variability of productivity in grassland communities, demonstrating that the insurance hypothesis applies also across space. Plant productivity varies due to environmental heterogeneity, and theory suggests that plant diversity can reduce this variation. While there is strong evidence of diversity effects on temporal variability of productivity, whether this mechanism extends to variability across space remains elusive. Here we determine the relationship between plant diversity and spatial variability of productivity in 83 grasslands, and quantify the effect of experimentally increased spatial heterogeneity in environmental conditions on this relationship. We found that communities with higher plant species richness (alpha and gamma diversity) have lower spatial variability of productivity as reduced abundance of some species can be compensated for by increased abundance of other species. In contrast, high species dissimilarity among local communities (beta diversity) is positively associated with spatial variability of productivity, suggesting that changes in species composition can scale up to affect productivity. Experimentally increased spatial environmental heterogeneity weakens the effect of plant alpha and gamma diversity, and reveals that beta diversity can simultaneously decrease and increase spatial variability of productivity. Our findings unveil the generality of the diversity-stability theory across space, and suggest that reduced local diversity and biotic homogenization can affect the spatial reliability of key ecosystem functions.

… productivity across climatic space, we defined ten quantile classes for MAT and TAP (together representing 100 units in climate space… 100 climate quantile regressions is indicated on …