Vegetation is one of the basic components of terrestrial ecosystems and a regulator of climate change (Alkama et al., 2022, Griscom et al., 2017). Since the 1980 s, the global leaf area index (LAI) based on satellite observations has shown a significant increasing trend, which is widely known as greening (Piao et al., 2020a). Numerous studies have confirmed the greening phenomenon, investigated the drivers and corresponding influences (Chen et al., 2019a, Chen et al., 2022b, Zhu et al., 2016). These studies found that global greening could reduce climate warming by increasing terrestrial carbon sequestration and cooling the surface (Chen et al., 2019b, Zeng et al., 2018).

However, the conclusion of greening has recently been challenged, with some studies finding that global greening stagnated or even turned browning after 2000 (Chen et al., 2022a, Liu et al., 2023, Pan et al., 2018, Yuan et al., 2019). Therefore, more and more attention has been paid to whether global vegetation is continuously greening or turning browning in recent years (Jiang et al., 2017, Wang et al., 2022). However, the results seem to be sensitive to the chosen datasets with different versions or sources. For example, Enhanced Vegetation Index (EVI) of MODIS showed opposite trends in version 5 and version 6 (Zhang et al., 2017). AVHRR-LAI illustrated global browning after 2000 (Chen et al., 2022a), which was in contrast to previous findings based on MODIS-LAI (Chen et al., 2019a).

The inconsistency of these findings has also led to widespread controversy among studies on the identification of key drivers. Studies supporting global browning primarily attribute browning to increased drought stress, and report potential mechanisms such as a sharp increase in atmospheric saturated vapor pressure difference (VPD) limiting vegetation growth (Yuan et al., 2019), excessive optimal temperatures inhibiting vegetation photosynthesis (Chen et al., 2022a), increasing water restriction on vegetation (Jiao et al., 2021), and reduced CO2 fertilization effects due to water and nutrient availability (Wang et al., 2020). However, process-based models and observations suggest that global vegetation is still positively affected by CO2 fertilization and land management, making greening dominant (Chen et al., 2019a, Zhu et al., 2016). These controversies not only increase the uncertainty in the estimation of global terrestrial carbon sources and sinks, but also hinder the better understanding of processes within terrestrial carbon cycle, which is of critically importance for the development of models to describe these processes.

Several key LAI datasets have been significantly updated recently, providing an opportunity to re-examine global vegetation change trends and their drivers over nearly two decades. The widely used GIMMS-LAI3g has been further updated to GIMMS-LAI4g, which solves a series of problems such as sensor drift (Cao et al., 2023). GLASS-LAI has also been updated to version 6, which shows higher accuracy compared to other products (Ma and Liang, 2022). The update of these products may help to reduce the uncertainty in the analysis of global vegetation change trends after 2000.

In addition, linear regression is often used to analyze the vegetation change trend in the current study. However, the vegetation change trend in the later period may be covered up in the case of widespread greening (or browning) in the earlier period (Pan et al., 2018). Some studies use piecewise linear regression to characterize differences in vegetation change over different time periods, but this limits the application of some data sets with shorter time series (Chen et al., 2020, Wang et al., 2011). Therefore, another potential problem is how to determine a reliable indicator of whether the vegetation change trend has changed over a short period of time.

In order to explore these issues, in this study, we analyzed the global vegetation change trends from 2001 to 2020 based on the latest version of LAI datasets. Importantly, we also introduced the concept of LAI growth rate to analyze the rate of greening (browning). Finally, we further analyzed the drivers of LAI trend and LAI growth rate trend.