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Vegetation greening in China has been extensively examined, but little is known about its seasonal characteristics and its association with soil moisture (SM) and temperature changes. Using high-resolution (0.1°, 8 d) datasets of leaf area index (LAI), together with SM, soil temperature (ST) datasets, and the dominance analysis method, this study is designed to detect seasonal vegetation changes across China during 1981–2018 and its links to climate change. The results show that 56.8% of land area across China experienced a greening trend while 6.6% browning trend through 1981–2018. LAI increasing area expanded to a maximum of 59.3% in spring and the decreasing area reached a maximum of 10.6% in autumn. Spring increases in LAI in main vegetation regions were significantly correlated with positive ST anomalies, while autumn decreases in LAI except sparsely vegetated regions were correlated with negative SM anomalies. Combined SM and temperature anomalies explain 10.9% of the observed LAI changes, which is 4 times larger than that directly explained by precipitation and surface air temperature (2.7%). The warming of soil under climate change was driving the LAI increases, while drying was largely responsible for LAI decreases. These findings provide further evidence of climate change impacts on regional ecosystems and highlight the importance of soil heat and water conditions in translating global warming signals.

There is consistent evidence of vegetation greening in Central Asia over the past four decades. However, in the early 1990s, the greening temporarily stagnated and even for a time reversed. In this study, we evaluate changes in the normalized difference vegetation index (NDVI) based on the long-term satellite-derived remote sensing data systems of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI from 1981 to 2013 and MODIS NDVI from 2000 to 2020 to determine whether the vegetation in Central Asia has browned. Our findings indicate that the seasonal sequence of NDVI is summer > spring > autumn > winter, and the spatial distribution pattern is a semicircular distribution, with the Aral Sea Basin as its core and an upward tendency from inside to outside. Around the mid-1990s, the region’s vegetation experienced two climatic environments with opposing trends (cold and wet; dry and hot). Prior to 1994, NDVI increased substantially throughout the growth phase (April–October), but this trend reversed after 1994, when vegetation began to brown. Our findings suggest that changes in vegetation NDVI are linked to climate change induced by increased CO2. The state of water deficit caused by temperature changes is a major cause of the browning turning point across the study area. At the same time, changes in vegetation NDVI were consistent with changes in drought degree (PDSI). This research is relevant for monitoring vegetation NDVI and carbon neutralization in Central Asian ecosystems.

Satellite-derived vegetation records (GIMMS3g-NDVI) report that climate warming promotes vegetation greening trends; however, the climate impacts on vegetation growth during the global warming hiatus period (1998–2012) remain unclear. In this study, we focused on the vegetation change trend in Xinjiang in spring and autumn before and during the recent warming hiatus period, and their climate-driving mechanisms, which have not been examined in previous studies. Based on satellite records, our results indicated that the summer normalized difference vegetation index (NDVI) in Xinjiang experienced a greening trend, while a browning trend existed in spring and autumn during this period. The autumn NDVI browning trend in Xinjiang was larger than that in spring; however, the spring NDVI displayed a higher correlation with climatic factors than did the autumn NDVI. During the warming hiatus, spring climatic factors were the main controlling factors of spring NDVI, and spring vapor pressure deficit (VPD) had the highest positive correlation with spring NDVI, followed by spring temperature. The larger increase in air temperature in spring than in autumn resulted in increased VPD differences in spring and autumn. In autumn, summer climatic factors (e.g., VPD, WS, RH, and precipitation) were significantly correlated with the autumn NDVI during the warming hiatus. However, the autumn temperature was weakly correlated with the autumn NDVI. Our results have significant implications for understanding the response of vegetation growth to recent and future climatic conditions.

Vegetation greening resulting from ecological engineering efforts has statistically contributed to environmental improvement, through enhancing ecosystem effectiveness remains a challenge. Nevertheless, there has been a notable lack of research dedicated to enhancing vegetation resilience and restoration potential by mitigating vegetation browning in watersheds within arid and semi-arid regions. This study fills that gap by identifying the spatial heterogeneity in ecological resilience using statistical analyses and an exponential decay approach. It then evaluates the potential for ecological restoration by optimizing ecosystem structures in browning areas based on resilience and reference state. The key findings included the following: (1) With a narrower interquartile range, kNDVI values from 2015 to 2023 demonstrated notable increases as compared to 2000–2014. The northern and eastern sub-watersheds showed greater vegetation restoration, but the southern regions showed less resilience. (2) Vegetation resilience in the majority of sub-watersheds was concentrated at moderate levels, and the number of grids with strong positive tendencies decreased, according to the analysis of grid trends. (3) Fifteen reference states were established for browning areas based on the current natural conditions. The larger restoration potential ratio showed notable differences in sub-watershed restoration, indicating opportunities for improvement. Extreme vegetation degradation demonstrated little potential for restoration in resource-poor areas. This study provides valuable insights into integrating resilience and restoration potential into ecological restoration practices, advancing the application of ecological engineering strategies.

Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 1982-2015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 1982-2015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 1999-2015 compared to 1982-1998. Trend analysis within the growing season reveals that sea ice decline was larger in spring for the 1982-1998 period compared to 1999-2015, while fall sea ice decline was larger in the later period. Land surface temperature trends for the 1982-1998 growing season are positive and for 1999-2015 are positive in May-June but weakly negative in July-August. Spring biweekly NDVI trends are positive and significant for 1982-1998, consistent with increasing open water and increased available warmth in spring. MaxNDVI trends for 1999-2015 display significant negative trends in May and the first half of June. Numerous possible drivers of early growing season NDVI decline coincident with warming temperatures are discussed, including increased standing water, delayed spring snow-melt, winter thaw events, and early snow melt followed by freezing temperatures. Further research is needed to robustly identify drivers of the spring NDVI decline.

China has experienced substantial climate change during past decades. To understand the response of forests to this change, we investigated the trends in forest growth and the control mechanism behind the observed variations in the North-South Transect of Eastern China (NSTEC). Interpretations were made based on the Normalized Difference Vegetation Index (NDVI) and temperature and precipitation data from 1982 to 2006. Our results indicated that the growing season NDVI trend showed a significant linear relationship with the mean growing season temperature and precipitation trend exhibiting inconsistent or even opposite performances from the north to south of the NSTEC. Prevalent forest greening was observed in the cold and dry regions where the climate becomes warmer and drier, while forest browning appeared to dominate in the warm and humid areas where climate turns warmer and wetter. These phenomena indicated the positive effect of growing season climates on forest growth may stall under warmer and wetter conditions in the much warmer and wetter regions. Our findings showed a difference in growth trend between needle leaf forests and broadleaf forests. In the cold and dry regions, the NDVI of most needle leaf forests showed an increasing trend, but nearly half of the broadleaf forests exhibited a negative NDVI slope while the other broadleaf forests exhibited a positive NDVI slope.

In order to monitor vegetation growth and development over the districts and land covers of Tamil Nadu, India during the crop growing season viz., Khairf and Rabi of 2017, Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance product (MOD09A1) which is available at 500 m resolution and 8-day temporal period was used to derive a time series based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for monitoring and mapping terrestrial vegetation trend analysis which showed areas in Tamil Nadu having vegetation greening and vegetation browning. The regression slope values derived from the trend analysis was utilized and the NDVI and NDWI seasonal trend showed majority of area in Tamil Nadu falling under positive trend during the Kharif season (86.52 per cent for NDVI and 90.29 per cent for NDWI). While irrespective of land cover classes, NDVI and NDWI during Kharif season showed a greater positive trend (greening) with least negative trend (browning) for vegetation growth over the land covers whereas during Rabi season it was observed to have a mix of positive trend and negative trend over the land covers. This study was carried out to show that a systematic study can be done for understanding changes over the landscape through the use of high spatial resolution satellite dataset such as MODIS, which provides detailed spatial and temporal description at regional scale. While a trend analysis using regression slope values can be considered for demonstrating the spatial and temporal consistency on land and vegetation dynamics.

Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.

Revealing the response of vegetation on the Qinghai-Tibet Plateau (QTP) to climate change and human activities is crucial for ensuring East Asian ecological security and regulating the global climate. However, the current research rarely explores the time-lag effects of climate on vegetation growth, leading to considerable uncertainty in analyzing the driving mechanisms of vegetation changes. This study identified the main driving factors of vegetation greenness (vegetation index, EVI) changes after investigating the lag effects of climate. By analyzing the trends of interannual variation in vegetation and climate, the study explored the driving mechanisms behind vegetation changes on the QTP from 2000 to 2020. The results indicate that temperature and precipitation have significant time-lag effects on vegetation growth. When considering the lag effects, the explanatory power of climate on vegetation changes is significantly enhanced for 29% of the vegetated areas. About 31% of the vegetation on the QTP exhibited significant “greening”, primarily in the northern plateau. This greening was attributed not only to improvements in climate-induced hydrothermal conditions but also to the effective implementation of ecological projects, which account for roughly half of the significant greening. Only 2% of the vegetation on the QTP showed significant “browning”, sporadically distributed in the southern plateau and the Sanjiangyuan region. In these areas, besides climate-induced drought intensification, approximately 78% of the significant browning was due to unreasonable grassland utilization and intense human activities. The area where precipitation dominates vegetation improvement was larger than the area dominated by temperature, whereas the area where precipitation dominates vegetation degradation is smaller than that where temperature dominates degradation. The implementation of a series of ecological projects has resulted in a much larger area where human activities positively promoted vegetation compared to the area where they negatively inhibited it.