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The considerable interest in detecting global vegetation changes based on satellite observations is increasing. However, studies rely on single indices to explore the driving mechanisms of the greening trend might exacerbate uncertainties of global ecosystem change. Thus, vegetation growth dynamics from various biophysical properties required to be monitored comprehensively. In this study, a consistent framework for evaluating vegetation growth trends was developed based on five widely used satellite‐derived products of MODIS Collection 6; the consistency in vegetation growth was mapped; and the factors that affected the consistency of vegetation growth were explored. The results showed that, during 2000‐2015, 45.6% of global vegetated area experienced inconsistent trends in vegetation greenness, cover and productivity, especially in evergreen broadleaf forests, grasslands, open shrublands, woody savannas and croplands. Only 5.4% of global vegetated area exhibited simultaneous trends in greenness, cover and productivity, and the inconsistent areas were expanding in the study period. Contradictory vegetation changes were mainly reflected in the opposite trends of vegetation greenness and productivity in evergreen broadleaf forests. Moreover, the inconsistency change was mainly manifested in the greenness‐dominated vegetation enhancement, without enhanced productivity. The increment difference between NPP and GPP also showed respiration losses greatly offset the effect of vegetation greenness or cover on productivity. This study provides integrated insights for understanding the inconsistency of vegetation structural and functional changes in the context of global greening. Plain Language Summary Terrestrial vegetation dynamics are extremely important to global environmental change and have consequences for the functioning of the Earth system and provisioning of ecosystem services. Recent greening of the global terrestrial ecosystems suggested an increasing trend in vegetation growth. However, different vegetation properties that were described by indices have not been comprehensively compared. In this study, a consistent framework for evaluating vegetation growth trends was developed based on five widely used satellite‐derived vegetation indices; the consistency in vegetation growth was mapped; and the factors that affected the consistency of vegetation growth were explored. We found that during 2000–2015, nearly half of global vegetated area experienced inconsistent trends in vegetation greenness, cover, and productivity, especially in evergreen broadleaf forests. The vegetation inconsistent change was manifested in the greenness‐dominated vegetation enhancement, but the productivity did not enhance. Relationship between vegetation cover and productivity was higher than that between vegetation greenness and productivity. It was also found that respiration losses greatly offset the effect of vegetation greenness or cover on productivity. This study provides integrated insights into vegetation growth trends, interpreting inconsistency of vegetation structural and functional changes in the context of global greening. Key Points Nearly half of global vegetated area experienced inconsistent vegetation trends Inconsistency was manifested as the greenness and non‐productivity enhancement Vegetation types differed in the greenness, cover, and productivity trends

The Qinghai–Tibet Plateau (QTP), which is known as Earth’s “Third Pole”, is a driver of global climate change, and long-term monitoring of QTP vegetation can reveal changes attributable to climatic and human influences. Previous research monitoring vegetation on the QTP focused primarily on spatiotemporal variations of vegetation indices, while few studies have considered fractional vegetation cover (FVC) in relation to topographic and frozen soil factors. We used MODIS-EVI, digital elevation models, and frozen soil data to investigate topographic effects on vegetation growth status in different soil types on the QTP during 2000–2020. (1) FVC showed a trend of increase during 2000–2020, and the FVC on the QTP decreased from the southeast to the northwest in spatial distribution. FVC in permafrost regions was the lowest, followed by seasonal frozen soil areas; FVC in unfrozen areas was the highest. (2) With increasing elevation, FVC of permafrost, seasonal frozen, and unfrozen soil areas showed downward trends for each aspect. In seasonal frozen soil areas, at elevation ≤4000 m (>4000 m), FVC of sunny (shady) slopes was greater than that of shady (sunny) slopes. In permafrost regions, except at elevations of 3000–4000 m, FVC of shady slopes was greater than that of sunny slopes. In unfrozen soil areas, at elevation >4000 m, FVC of sunny slopes was obviously greater than that of shady slopes. (3) With increasing slope, FVC in seasonal frozen and permafrost soil (unfrozen soil) regions showed a trend of increase (decrease). In seasonal frozen soil areas, FVC of sunny (shady) slopes was greater than that of shady (sunny) slopes on slopes ≤6° (>6°). In permafrost regions, FVC of sunny slopes was less than that of shady slopes. With increasing slope, the influence of aspect became more obvious. In unfrozen soil areas, FVC of sunny slopes was slightly greater than that of shady slopes. Topographic effects especially the elevation and slope effects might significantly affect the spatiotemporal variations of vegetation growth status in frozen soil regions on the QTP.

Vegetation dynamics are often affected by climate variability, but the past state of vegetation has a non-negligible impact on current vegetation growth. However, seasonal differences in the effects of these drivers on vegetation growth remain unclear, particularly in ecologically fragile areas. We used the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and leaf area index (LAI) to describe the vegetation dynamic in the upper and middle reaches of the Yellow River basin (YRB). Three active vegetation growing seasons (early, peak, and late) were defined based on phenological metrics. In light of three vegetation indicators and the climatic data, we identified the correlation between the inter-annual variation of vegetation growth in the three sub-seasons. Then, we quantified the contributions of climate variability and the vegetation growth carryover (VGC) effect on seasonal vegetation greening between 2000–2019. Results showed that both the vegetation coverage and productivity in the study area increased over a 20-year period. The VGC effect dominated vegetation growth during the three active growing seasons, and the effect increased from early to late growing season. Vegetation in drought regions was found to generally have a stronger vegetation carryover ability, implying that negative disturbances might have severer effects on vegetation in these areas. The concurrent seasonal precipitation was another positive driving factor of vegetation greening. However, sunshine duration, including its immediate and lagged impacts, had a negative effect on vegetation growth. In addition, the VGC effect can sustain into the second year. The VGC effect showed that initial ecological restoration and sustainable conservation would promote vegetation growth and increase vegetation productivity. This study provides a comprehensive perspective on understanding the climate–vegetation interactions on a seasonal scale, which helps to accurately predict future vegetation dynamics over time in ecologically fragile areas.

As an important land-surface parameter, vegetation phenology has been estimated from observations by various satellite-borne sensors with substantially different spatial resolutions, ranging from tens of meters to several kilometers. The inconsistency of satellite-derived phenological metrics (e.g., green-up date, GUD, also known as the land-surface spring phenology) among different spatial resolutions, which is referred to as the “scale effect” on GUD, has been recognized in previous studies, but it still needs further efforts to explore the cause of the scale effect on GUD and to quantify the scale effect mechanistically. To address these issues, we performed mathematical analyses and designed up-scaling experiments. We found that the scale effect on GUD is not only related to the heterogeneity of GUD among fine pixels within a coarse pixel, but it is also greatly affected by the covariation between the GUD and vegetation growth speed of fine pixels. GUD of a coarse pixel tends to be closer to that of fine pixels with earlier green-up and higher vegetation growth speed. Therefore, GUD of the coarse pixel is earlier than the average of GUD of fine pixels, if the growth speed is a constant. However, GUD of the coarse pixel could be later than the average from fine pixels, depending on the proportion of fine pixels with later GUD and higher growth speed. Based on those mechanisms, we proposed a model that accounted for the effects of heterogeneity of GUD and its co-variation with growth speed, which explained about 60% of the scale effect, suggesting that the model can help convert GUD estimated at different spatial scales. Our study provides new mechanistic explanations of the scale effect on GUD.

Understanding how the dynamics of vegetation growth respond to climate change at different temporal and spatial scales is critical to projecting future ecosystem dynamics and the adaptation of ecosystems to global change. In this study, we investigated vegetated growth dynamics (annual productivity, seasonality and the minimum amount of vegetated cover) in China and their relations with climatic factors during 1982–2011, using the updated Global Inventory Modeling and Mapping Studies (GIMMS) third generation global satellite Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset and climate data acquired from the National Centers for Environmental Prediction (NCEP). Major findings are as follows: (1) annual mean NDVI over China significantly increased by about 0.0006 per year from 1982 to 2011; (2) of the vegetated area in China, over 33% experienced a significant positive trend in vegetation growth, mostly located in central and southern China; about 21% experienced a significant positive trend in growth seasonality, most of which occurred in northern China (>35°N); (3) changes in vegetation growth dynamics were significantly correlated with air temperature and precipitation (p < 0.001) at a region scale; (4) at the country scale, changes in NDVI was significantly and positively correlated with annual air temperature (r = 0.52, p < 0.01) and not associated with annual precipitation (p > 0.1); (5) of the vegetated area, about 24% showed significant correlations between annual mean NDVI and air temperature (93% positive and remainder negative), and 12% showed significant correlations of annual mean NDVI with annual precipitation (65% positive and 35% negative). The spatiotemporal variations in vegetation growth dynamics were controlled primarily by temperature and secondly by precipitation. Vegetation growth was also affected by human activities; and (6) monthly NDVI was significantly correlated with the preceding month’s temperature and precipitation in western, central and northern China. The effects of a climate lag of more than two months in southern China may be caused mainly by the abundance of precipitation. These findings suggest that continuing efforts to monitor vegetation changes (in situ and satellite observations) over time and at broad scales are greatly needed, and are critical for the management of ecosystems and adapting to global climatic changes. It is likewise difficult to predict well future vegetation growth without linking these observations to mechanistic terrestrial ecosystem processes models that integrate all the satellite and in situ observations.

Recent research has demonstrated that global warming significantly enhances peak vegetation growth on the Tibetan Plateau (TP), underscoring the influence of climatic factors on vegetation dynamics. Nevertheless, the effects of different drought types on peak vegetation growth remain underexplored. This study utilized satellite-derived gross primary productivity (GPP) and the normalized difference vegetation index (NDVI) to assess the impacts of soil moisture (SM) and vapor pressure deficit (VPD) on peak vegetation growth (GPPmax and NDVImax) across the TP from 2001 to 2022. Our findings indicate that NDVImax and GPPmax exhibited increasing trends in most regions, displaying similar spatial patterns, with 65.28% of pixels showing an increase in NDVImax and 72.98% in GPPmax. In contrast, the trend for SM primarily showed a decrease (80.86%), while VPD showed an increasing trend (74.75%). Through partial correlation analysis and ridge regression, we found that peak vegetation growth was significantly affected by SM or VPD in nearly 20% of the study areas, although the magnitude of these effects varied considerably. Furthermore, we revealed that hydrothermal conditions modulated the responses of peak vegetation growth to SM and VPD. In regions with annual precipitation less than 650 mm and an annual mean temperature below 10 °C, decreased SM and increased VPD generally inhibited peak vegetation growth. Conversely, in warm and humid areas, lower SM and higher VPD promoted peak vegetation growth. These findings are crucial for deepening our understanding of vegetation phenology and its future responses to climate change.

Land surface phenology is a response of vegetation to local climate and to climate change, leading to crucial impacts on plant growth rhythm and productivity. Differences in vegetation growth activities in earlier and latter parts of the growing season are tightly correlated to phenological changes and the temporal distribution of plant productivity. However, its spatiotemporal pattern and climatic constraints are poorly understood. For Northeast China (NEC), long-term remotely-sensed vegetation greenness records (NDVI) were employed to quantify seasonally asymmetrical characteristics of vegetation growth in detail, which consists of asymmetry in growing rate (AsyR), mean vegetation greenness (AsyV), and growing period length (AsyL) during vegetation green up and senescence stages (simply termed as spring and autumn). Furthermore, the impact of temperature and precipitation on these indices were examined using relative importance analysis. The results indicate these asymmetric metrics present a pronounced interannual variability profile with a potential cycle of ten years (significant in AsyV and AsyR) for the entire NEC. AsyV is changing synchronously with AsyL but asynchronously with AsyR. The geographical distribution of asymmetric indices shows a similar pattern to identified vegetation cover types, especially in distinguishing crops from natural vegetation. Spatial-averaged asymmetric indices indicate spring production is greater than autumn production (reflected by negative AsyV) across most vegetation types in NEC, yet autumn is longer than spring in all vegetation types, which is identified by positive AsyL. Negative AsyR is mainly found in forests implying there is rapid green up and slow senescence in trees. From a temporal perspective, AsyV decreases with time in forested regions but increases in cropland and grassland, which is similar to the pattern for AsyL. AsyR primarily exhibits a positive trend in forest and a negative trend in cropland and grassland. A relative importance analysis indicates that asymmetries of temperature (AsyTemp) and precipitation (AsyPrcp) play an equal role in significantly affecting vegetation asymmetries in greenness and growth rate but are insignificant to growing season length. AsyTemp mainly presents an obvious contribution to changes in AsyR and AsyV over cropland and grassland. AsyPrcp shows a more widespread controlling effect on AsyR and AsyV over the NEC, except in eastern broad-leaved forest. For the entire NEC, asymmetries of temperature and precipitation are negatively correlated with AsyR but are positively correlated with AsyV and AsyL. This finding may imply that a warmer (positive AsyTemp) autumn tends to improve the length and intensity of vegetation activity. Thus, the long-term change in vegetation growth asymmetries may provide insights for the altering functions of ecosystems and provide information to more accurately build plant growth models in the context of global climate change. Additionally, when combined with other information, asymmetric indices can serve as a supporting tool in classification of vegetation types.

Under the ecological restoration project, the vegetation in the mining area shows a significant improvement trend. Exploring the causal relationship among the implementation of ecological restoration projects in mining areas, vegetation restoration, and the improvement of ecosystem service functions is of great significance for the current green development of coal mines. Therefore, in this study, we used the kernel Normalized Vegetation Index (kNDVI) to measure how vegetation growth has changed since ecological restoration projects began. Changes in four major ecosystem service functions, including soil conservation, net primary productivity (NPP), water yield, and habitat quality, were assessed before and after the restoration projects. The relationship between kNDVI and ecosystem services was further discussed by using the constraint line method. The results show the following: (1) Under the implementation of ecological restoration projects from 1994 to 2022, the annual vegetation growth rate in the mining area has progressively risen each year at a rate of 0.0046/a. Spatially speaking, 90.44% of the mining area had a substantial upward trend, indicating clear evidence of vegetation restoration. (2) Under the scientific ecological restoration of the mining areas, the total ecosystem service index increased from 0.41 in 1994 to 0.49 in 2022. The functions of ecosystem services have been enhanced to differing extents. (3) KNDVI’s constraint effect on the four ecosystem services changed dramatically before and after the ecological restoration effort. After the ecological restoration project, kNDVI’s constraint on ecosystem services decreased. (4) After restoration, the threshold value of kNDVI for maximizing the benefits of the four ecosystem services ranges from 0.1 to 0.2, and the constraint on the total ecosystem services reaches the threshold value of 0.225. This study employs more comprehensive data to examine the intricate relationship between environmental change and service function, which is crucial for the scientific management of ecological processes and facilitates the sustainable green development of mining areas.

Understanding the seasonality of vegetation growth is important for maintaining sustainable development of grassland livestock systems over the Tibetan Plateau (TP). Current knowledge of changes in the seasonality of TP grasslands is restricted to spring and autumn phenology, with little known about the date of peak vegetation growth, the most relevant quantity for grassland productivity. We investigate the shifts of the date of peak vegetation growth and its climatic controls for the alpine grasslands over the TP during 2001-2020 using a framework based on the law of minimum, which is based on the assumption that peak vegetation growth would be consistent with the peak timing of the most limiting climatic resource. The date of peak vegetation growth over the TP advanced by 0.81 days decade-1 during 2001-2020. This spring-ward shift mainly occurs in the semi-humid eastern TP, where the peak growth date tracks the advancing peak precipitation, and shifted towards the timing of peak temperature. The advancing peak growth over the eastern TP significantly stimulated the ecosystem production by 1.99 gCm-2 year-1 day-1 during 2001-2020, while this positive effect weakened from 3.02 gCm-2 year-1 day-1 during 2000s to 1.25 gCm-2 year-1 day-1 during 2010s. Our results highlighted the importance of water availability in vegetation growth over the TP, and indicated that the TP grassland is moving towards a tipping point of transition from thermal-constrained to water-constrained ecosystem under the rapid warming climate.

Vegetation growth is influenced not only by current climatic conditions but also by growth-enhancing signals and preceding climate factors. Taking the dominant species, Juniperus seravschanica Kom, in Tajikistan as the research subject, this study combines tree-ring width data with early xylem growth season data (from the start of xylem growth to the first day of the NDVI peak month), simulated using the Vaganov–Shashkin (V-S) model, a process-based tree-ring growth model. This study aims to explore the effects of vegetation growth carryover (VGC) and lagged climatic effects (LCE) on tree rings and the early xylem growth season at two different scales by integrating tree-ring width data and xylem phenology simulations. A vector autoregression (VAR) model was employed to analyze the response intensity and duration of VGC and LCE. The results show that the VGC response intensity in the early xylem growth season is higher than that of tree-ring width. The LCE duration for both the early xylem growth season and tree-ring width ranges from 0 to 11 (years or seasons), with peak LCE response intensity observed at a lag of 2–3 (years or seasons). The persistence of the climate lag effect on vegetation growth has been underestimated, supporting the use of a lag of 0–3 (years or seasons) to study the long-term impacts of climate. The influence of VGC on vegetation growth is significantly stronger than that of LCEs; ultimately indicating that J. seravschanica adapts to harsh environments by modulating its growth strategy through VGC and LCE. Investigating the VGC and LCE of multi-scale xylem growth indicators enhances our understanding of forest ecosystem dynamics.