12.9.2019

Characteristics, drivers and feedbacks of global greening

Abstract

Vegetation greenness has been increasing globally since at least 1981, when satellite technology enabled large-scale vegetation monitoring. The greening phenomenon, together with warming, sea-level rise and sea-ice decline, represents highly credible evidence of anthropogenic climate change. In this Review, we examine the detection of the greening signal, its causes and its consequences. Greening is pronounced over intensively farmed or afforested areas, such as in China and India, reflecting human activities. However, strong greening also occurs in biomes with low human footprint, such as the Arctic, where global change drivers play a dominant role. Vegetation models suggest that CO2 fertilization is the main driver of greening on the global scale, with other factors being notable at the regional scale. Modelling indicates that greening could mitigate global warming by increasing the carbon sink on land and altering biogeophysical processes, mainly evaporative cooling. Coupling high temporal and fine spatial resolution remote-sensing observations with ground measurements, increasing sampling in the tropics and Arctic, and modelling Earth systems in more detail will further our insights into the greening of Earth.

Key points

  • Long-term satellite records reveal a significant global greening of vegetated areas since the 1980s, which recent data suggest has continued past 2010.
  • Pronounced greening is observed in China and India due to afforestation and agricultural intensification.
  • Global vegetation models suggest that CO2 fertilization is the main driver of global vegetation greening.
  • Warming is the major cause of greening in boreal and Arctic biomes, but has negative effects on greening in the tropics.
  • Greening was found to mitigate global warming through enhanced land carbon uptake and evaporative cooling, but might also lead to decreased albedo that could potentially cause local warming.
  • Greening enhances transpiration, a process that reduces soil moisture and runoff locally, but can either amplify or reduce runoff and soil moisture regionally through altering the pattern of precipitation.

Introduction

Vegetation controls the exchange of carbon, water, momentum and energy between the land and the atmosphere, and provides food, fibre, fuel and other valuable ecosystem services1,2. Changes in vegetation structure and function are driven by climatic and environmental changes, and by human activities such as land-use change. Given that increased carbon storage in vegetation, such as through afforestation, could combat climate change3,4, quantifying vegetation change and its impact on carbon storage and climate has elicited considerable interest from scientists and policymakers. However, it is not possible to detect vegetation changes at the global scale using ground-based observations due to the heterogeneity of change and the lack of observations that can detect these changes both spatially and temporally. While monitoring the changes in some vegetation properties (for example, stem-size distribution and below-ground biomass) at the global scale remains impossible, satellite-based remote sensing has enabled continuous estimation of a few important metrics, including vegetation greenness, since the 1980s (Box 1). In 1986, a pioneering study by Tucker et al.5 on remotely sensed normalized difference vegetation index (NDVI; a radiometric measure of vegetation greenness) (Box 1) revealed a close connection between vegetation canopy greenness and photosynthesis activity (as inferred from seasonal variations in atmospheric CO2 concentration). This index was successfully used to constrain vegetation primary production globally6. Using NDVI data from 1981 to 1991, Myneni et al.7 reported an increasing trend in vegetation greenness in the Northern Hemisphere, which was subsequently observed across the globe8,9,10,11,12,13. This ‘vegetation greening’ is defined as a statistically significant increase in annual or seasonal vegetation greenness at a location resulting, for instance, from increases in average leaf size, leaf number per plant, plant density, species composition, duration of green-leaf presence due to changes in the growing season and increases in the number of crops grown per year. There has also been considerable interest in understanding the mechanisms or drivers of greening11,14. Lucht et al.14 and Xu et al.10 revealed that warming has eased climatic constraints, facilitating increasing vegetation greenness over the high latitudes. Zhu et al.11 further investigated key drivers of greenness trends and concluded that CO2 fertilization is a major factor driving vegetation greening at the global scale. Subsequent studies based on fine-resolution and medium-resolution satellite data13 have shown the critical role of land-surface history, including afforestation and agricultural intensification, in enhancing vegetation greenness. The large spatial scale of vegetation greening and the robustness of its signal have led the Intergovernmental Panel on Climate Change (IPCC) special report on climate change and land15 to list it, together with global-scale warming, sea-level rise16 and sea-ice decline16, as highly credible evidence of the environmental impact of anthropogenic climate change. Greener vegetation not only results from climatic and atmospheric changes but also feeds back to the climate through biogeochemical and biogeophysical processes. These feedbacks are often studied with Earth system models (ESMs), in which vegetation is coupled with the atmosphere and the hydrologic cycle17. ESM-based studies have demonstrated that greening can accelerate the hydrologic cycle by increasing the amount of water transpired by plants, alter the energy exchange between land and the atmosphere, and affect atmospheric circulation patterns18,19. In this Review, we synthesize past and recent efforts to characterize the spatiotemporal patterns of vegetation greening since the 1980s. We discuss how rising atmospheric CO2 concentration, climate change, land-use change and nitrogen deposition are the key drivers of greenness changes on the global and regional scale. We assess the impacts of vegetation greening on carbon, water and energy balances, and conclude by identifying key challenges and perspectives for future research.

Greenness changes

Global-scale vegetation greening has been demonstrated using nearly four decades of NDVI and leaf area index (LAI) greenness data derived from the Advanced Very-High-Resolution Radiometer (AVHRR) instrument (Fig. 1a,b). While early studies primarily used the NDVI to detect changes in global greenness, recent studies widely use the LAI, since it has clear physical interpretation and is a fundamental variable in almost all land-surface models (Box 1). An ensemble of LAI datasets has shown that 52% (P < 0.05) to 59% (P < 0.10) of global vegetated lands displayed an increasing trend in growing season LAI since the 1980s11 (Fig. 1a). Although some studies reported a stalling, or even a reversal, of the greening trend since 2000 based on AVHRR20 and collection 5 (C5) of the Moderate Resolution Imaging Spectroradiometer (MODIS) data21, this signal might be an artefact of sensor degradation and/or processing22,23,24. For example, using a revised calibration of the MODIS data in the most recent collection 6 (C6) dataset24, Chen et al.13 showed that leaf area increased by 5.4 million  km2 over 2000–2017, an area equivalent to the areal extent of the Amazon rainforest13. Indeed, 34% of vegetated land exhibited greening (P < 0.10), whereas only 5% experienced browning (P < 0.10), that is, a loss of vegetation greening.
Fig. 1: Changes in satellite-derived global vegetation indices, vegetation optical depth and contiguous solar-induced fluorescence.
figure1
a | Leaf area index (LAI) from four products: GIMMS13, GLASS192, GLOBMAP23 and Moderate Resolution Imaging Spectroradiometer (MODIS) C6 (ref.193). b | Normalized difference vegetation index (NDVI) from three products: GIMMS194, MODIS C6 (ref.195) and SPOT196. c | Enhanced vegetation index (EVI) from MODIS C6 (ref.195). d | Near-infrared reflectance of terrestrial vegetation (NIRv)197. e | Vegetation optical depth (VOD)119. f | Contiguous solar-induced fluorescence (CSIF)114. In parts a and b, the light-green shading denotes the range of LAI and NDVI across different products and the dark-green shading denotes the interquartile range (between the 25th and 75th percentiles). Only measurements during the growing season11 were considered.
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New satellite-based vegetation indices also support the global greening trend observed since 2000 (Fig. 1), including the enhanced vegetation index (EVI) and near-infrared reflectance of terrestrial vegetation (NIRv) (Box 1). However, while vegetation greenness is increasing at the global scale, the changes vary considerably between regions and seasons.

Regional trends

In the high northern latitudes (>50°N), AVHRR and Landsat records indicate a widespread increase in vegetation greenness since the 1980s8,12,25 (Fig. 2a–d). Regions with the greatest greening trend include northern Alaska and Canada, the low-Arctic parts of eastern Canada and Siberia, and regions of Scandinavia12,25,26. Dendrochronological data and photographic evidence further corroborate these findings27,28,29,30. In general, the LAI over high northern latitudes will continue to increase by the end of this century31, based on the results of an ensemble of ESMs (Fig. 2e–h). However, although only 3% of the high latitudes show browning during 1982–2014 (ref.25), there is a growing proportion of Arctic areas exhibiting a browning trend32. Such trends first emerged in boreal forests, where a multitude of disturbances (for example, fires, harvesting and insect defoliation) prevail9,33,34,35,36,37. The North American boreal forests in particular exhibit browning areas nearly 20 times larger than the Eurasian boreal forests, showing heterogeneous regional greenness change38.
Fig. 2: Spatial patterns of changes in leaf area index.
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a | Growing season (GS) mean Advanced Very-High-Resolution Radiometer (AVHRR) leaf area index (LAI) trend during 1982–2009. The AVHRR LAI dataset is the average of three different products (GIMMS13, GLOBMAP23 and GLASS192). b | Change in the GS mean AVHRR LAI over four regions during 1982–2009. c | GS mean Moderate Resolution Imaging Spectroradiometer (MODIS) LAI during 2000–2018. d | Change in the GS mean MODIS LAI over four regions during 2000–2018. MODIS LAI is from collection 6 (ref.193). e | Relative change in GS mean LAI between 1981–2000 and 2081–2100 under the Representative Concentration Pathway 2.6 (RCP2.6), based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble. f | Relative change in GS mean CMIP5 LAI 2018–2100 under RCP2.6, relative to 1981–2000. g | Relative change in GS mean LAI between 1981–2000 and 2081–2100 under RCP8.5, based on CMIP5. h | Relative change in GS mean CMIP5 LAI 2018–2100 under RCP8.5, relative to 1981–2000. The number of CMIP5 models used in the calculation of the multi-model mean is 16 and 19, for RCP2.6 and RCP8.5, respectively (Supplementary Table S5). In parts a, c, e and g, the white land areas depict barren lands, permanent ice-covered areas, permanent wetlands, built-up areas and water. In parts b, d, f and h, blue represents the high-latitude Northern Hemisphere (NH) (50–90°N), green represents the temperate NH (25–50°N), purple represents the tropical zone (25°S–25°N) and yellow represents the extratropical Southern Hemisphere (SH) (90–25°S). The shading shows the ±1 inter-model standard deviation.
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The northern temperate region (25–50°N) is another vegetation greening hotspot, experiencing faster rates of greening than the high latitudes since 2000 (Fig. 2b,d). Indeed, ~14 million km2 of the temperate region greened (P < 0.10), contributing about one-half of the global net leaf area increase over this time period13. The increase of vegetation greenness is especially strong in agricultural regions (for example, India13) and recently afforested areas (for example, China13,39); collectively, China and India alone contribute more than 30% of the total net increase in the global LAI13. Tropical regions (25°S–25°N) are also greening (Fig. 2b,d), contributing about a quarter of the net global increase in leaf area since 2000 (ref.13). However, the tropics also have areas where significant browning has been reported, for example, in the Brazilian Cerrado and Caatinga regions and Congolian forests13,40. It is worth noting that substantial uncertainties remain in the tropical vegetation greenness estimations due to the saturation effects of greenness indices in dense vegetation41 and contamination by clouds and aerosols42. These uncertainties partly underlie the disagreement between the MODIS and AVHRR products13 when measuring tropical greenness and the debate on whether the Amazonian forests have greened or browned in response to droughts42,43,44. The extratropical Southern Hemisphere (>25°S) has experienced a general greening trend since the 1980s13,45, but it is lower than that in the temperate and high-latitude Northern Hemisphere13 (Fig. 2a–d). Regional greening hotspots in southern Brazil and southeast Australia mostly overlap with the intensive cropping areas13, highlighting the increasing contribution of managed ecosystems to vegetation greening. Note that most of this region is dominated by semi-arid ecosystems46, where vegetation coverage is generally sparse. Thus, satellite vegetation indices over this region are generally sensitive to change in soil background. For example, browning was detected from the AVHRR dataset since the 2000s20 (Fig. 2b), but MODIS C6 data (which is better calibrated and can distinguish vegetation from background more accurately) instead showed an overall greening trend particularly since 2002 (ref.13; Fig. 2c,d).

Seasonal changes of greenness

In the northern temperate and high latitudes, greenness often shows distinctive seasonal patterns within a calendar year (Fig. 3). Several metrics of land-surface phenology have been developed to depict the seasonal cycle of greenness47, including the widely used start of the growing season (SOS) and end of the growing season (EOS)48. Although phenology dates can vary depending on the greenness product or algorithm used49,50,51, significant trends towards both earlier SOS (2–8 days decade−1) and later EOS (1–6 days decade−1) and, thus, longer lengths of the growing season (LOS) (2–10 days decade−1), have been observed in most Northern Hemisphere regions during the past four decades7,8,25,52,53,54 (Fig. 3a–c). These trends are corroborated by ground-based observation data in spring and autumn55,56,57. The increase in LOS is driven mainly by an advanced SOS in Eurasia (53–81% of LOS lengthening is due to SOS advance) and delayed EOS in North America (57–96% of LOS lengthening is due to EOS delay), with the more rapid total LOS increase seen in Eurasia25,58,59,60.
Fig. 3: Changes in the seasonality of vegetation greenness and atmospheric CO2 concentration.
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a | Five-year mean seasonal variations of the normalized difference vegetation index (NDVI) over Northern Hemisphere high latitudes (>50oN) during 1982–1986 (black line) and 2008–2012 (green line). Start of the growing season (SOS) and end of the growing season (EOS) are shown as 50% of the maximum NDVI. The length of the growing season (LOS) is the difference between the EOS and the SOS. b | Frequency distribution of SOS change in the Northern Hemisphere during 1982–2012. c | Frequency distribution of EOS change in the Northern Hemisphere during 1982–2012. d | Five-year mean detrended seasonal CO2 variations at Barrow, AK, USA (71oN) (NOAA ESRL archive: https://www.esrl.noaa.gov/gmd/ccgg/obspack/) during 1980–1984 (black line) and 2013–2017 (green line). Vertical lines mark the spring zero-crossing date (SZC) and autumn zero-crossing date (AZC). Horizontal lines mark the seasonal amplitude as the difference between the maximum and the minimum of detrended seasonal CO2 variations. Shaded areas show the range of interannual variations in the NDVI in part a and the standard deviation of the detrended CO2 mole fraction (ppm) in part d at the day of year. NDVI data are the updated dataset from Tucker et al.194. Parts b and c are adapted with permission from ref.48, Wiley-VCH.
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In addition to longer growing seasons, satellite greenness data also reveal important shifts in the timing and magnitude of the seasonal peak greenness47,61. For example, the timing of peak greenness has advanced by 1.2 days decade−1 during 1982–2015 (ref.62) and 1.7 days decade−1 during 2000–2016 (ref.61) over the extratropical Northern Hemisphere (Fig. 3a), with the boreal region peak greenness advancing twice as fast as the Arctic tundra and temperate ecosystem peaks61. Since the 1980s, the magnitude of the peak greenness has also increased over the extratropical Northern Hemisphere by ~0.1 standardized NDVI anomaly per year62, with a stronger signal in the pan-Arctic region63,64. Phenology changes, including the SOS advancement, EOS delay and peak greenness enhancement, can significantly change the Earth’s seasonal landscape. Northern high latitudes, which traditionally have high seasonality (that is, short and intense growing seasons), are exhibiting seasonality similar to that of their counterparts 6° to 7° south in the 1980s. In other words, the latitudinal isolines of northern vegetation seasonality have shifted southward since the 1980s. The diminished seasonality of the northern high-latitude vegetation10 is consistent with changes in the velocity of vegetation greenness (defined as the ratio of temporal greenness change to its spatial gradient)65, which showed faster northward movement of the SOS (3.6 ± 1.0 km year−1) and the EOS (6.0 ± 1.1 km year−1) than the peak greenness (3.1 ± 1.0 km year−1) during 1982–2011 (ref.65).

Drivers of greening

Several factors are thought to impact vegetation greening, including rising atmospheric CO2 concentrations, climate change, nitrogen deposition and land-use changes. However, nonlinear impacts and interactions make it challenging to quantify the individual contribution of these factors to the observed greening trend. In this section, we review the contribution of several key drivers of vegetation greening and efforts to quantitatively attribute the observed greening trend to each of these factors.

CO2 fertilization

As CO2 is the substrate for photosynthesis, rising atmospheric CO2 concentration can enhance photosynthesis66 by accelerating the rate of carboxylation; this process is known as the ‘CO2 fertilization effect’. In addition, increased CO2 concentrations can also enhance vegetation greenness by partially closing leaf stomata, leading to enhanced water-use efficiency67, which should relax water limitation to plant growth, particularly over semi-arid regions45,68,69. Analysis of the ‘Trends and drivers of the regional-scale sources and sinks of carbon dioxide’ (TRENDY) ensemble of dynamic global vegetation models (DGVMs)70 suggests that rising CO2 is the dominant driver of vegetation greening, accounting for nearly 70% of global LAI trend since the 1980s11 (Fig. 4). Statistical modelling also supports the important role of rising atmospheric CO2 concentration in driving vegetation greening71,72. Free-air CO2 enrichment (FACE) experiments show that elevating the CO2 concentration by ~200 ppm above the ambient conditions significantly enhances vegetation productivity73 and increases leaf area74. Different plant species vary largely in the magnitude of LAI enhancement75, with the larger effect on forest stands having lower LAI at the ambient conditions76. In DGVMs, elevated CO2 increases vegetation productivity more in tropical ecosystems than in temperate and boreal ecosystems11,77,78 (Fig. 4b). However, the strength of the CO2 fertilization effect can be limited by extreme weather events79,80 and nutrient and water availability73,81,82. Indeed, nitrogen and phosphorus have been shown to regulate the global pattern of CO2 fertilization effects83. Since nutrient processes were under-represented in the ESMs used in the IPCC Fifth Assessment Report (AR5), the predictions of continued greening trends through 2100 (ref.31) (Figs 2e–h,5) might overestimate the CO2 fertilization effects.
Fig. 4: Attribution of trends in growing season mean leaf area index.
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a | Trends in the global-averaged leaf area index (LAI) derived from satellite observation (OBS) and attributed respectively to rising CO2 (CO2), climate change (CLI), nitrogen deposition (NDE) and land cover change (LCC) during 1982–2009 (ref.11). The error bars show the standard deviation of trends derived from satellite data and model simulations. Individual model-estimated contributions of each driver to LAI trends are shown as coloured dots. b | Contribution of different drivers to LAI change in latitude bands (>50°N, 25–50°N, 25°S–25°N, >25°S). c | Spatial distribution of the dominant driver of growing season mean LAI trend, defined as the driver that contributes the most to the increase (or decrease) in LAI in each vegetated grid cell. Other factors (OF) is defined by the fraction of the observed LAI trends not accounted for by modelled factors. Parts b and c share the same colour legend, where the ‘+’ prefix indicates a positive effect from the corresponding driver on LAI trends and the ‘−’ prefix indicates a negative effect. Data courtesy of Zhu et al.11. Part c adapted from ref.11, Springer Nature Limited.
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Fig. 5: Current and predicted global leaf area index.
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Current (observed 2000–2018) leaf area index (LAI) anomaly (m2 m−2) from an average of satellite measurements based on GIMMS13, GLASS192, GLOBMAP23 and Moderate Resolution Imaging Spectroradiometer (MODIS) C6 (ref.193). Predicted LAI anomalies from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model (Supplementary Table S5) projections during 2081–2100. The boxes and whiskers indicate the minimum, 10th, 25th, 50th, 75th and 90th percentiles and the maximum LAI of CMIP5 models; the black and white lines indicate the mean and median LAI of CMIP5 models, respectively. LAI anomalies were calculated against the average during 1980–2005.
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Climate change

Although rising atmospheric CO2 concentration is the main driver of global greening, climate change, such as anthropogenic warming and regional trends in precipitation, is a dominant driver of greenness changes over 28% of the global vegetated area11. The global contribution of climate change to increasing greenness is only 8% (Fig. 4a), however, because impacts of climate change on vegetation greenness vary between regions11. For example, warming could reduce vegetation growth in the tropics84, where ambient temperature is close to vegetation optimal temperature85, but warming significantly increases vegetation greenness in the boreal and Arctic regions86 by enhancing metabolism87 and extending the growing season59,88,89. DGVM simulations show that the positive effects of climate change, primarily from warmer temperature14, dominate the greening trend over more than 55% of the northern high latitudes (Fig. 4b) and in the Tibetan Plateau11. However, this positive impact of anthropogenic warming on greenness appears to have weakened during the past four decades90,91, when the correlation coefficient between temperature and greenness decreased by more than 50%90,91, suggesting a possible saturation of future greening in response to warmer temperature. In water-limited ecosystems, changes in precipitation — reflecting either decadal climate variability or trends from anthropogenic climate change — were suggested as the main driving factor of greening and browning45,92. Precipitation-driven greening is most evident in the African Sahel93,94 and semi-arid ecosystems of southern Africa and Australia45,95 (Fig. 4c). Both empirical models and DGVMs indicate that ‘the greening Sahel’, one of the early examples of vegetation greening detected by satellite measurements93,94, was primarily driven by increases in precipitation after a severe drought in the early 1980s96,97,98. This causal relationship between precipitation and greenness changes was further supported through analyses of recent microwave satellite measurements and long-term field surveys99,100.

Land-use change

Like climate change, land-use change exerts a considerable but highly spatially variable influence on greenness changes11,13 (Fig. 4). Specifically, deforestation dominates the tropics101,102, while afforestation increases forest area over temperate regions, particularly in China, where the forest area has increased by more than 20% since the 1980s103. The TRENDY ensemble of DGVMs70 indicates that greenness changes over 19% of the northern temperate vegetation (25–50°N) are primarily driven by land-use change11 (Fig. 4c). However, this might be an underestimate since critical land-use processes104,105 are under-represented or missing in the current generation of DGVMs. For example, forest-age dynamics are not represented in most DGVMs, even though one-third of the global forests are younger than 20 years old106, implying that forest regrowth might contribute to global greening in the future. In addition, agricultural intensification with multiple cropping, irrigation and fertilizer usage must contribute considerably to vegetation greening, which is exemplified by the dominance of other unmodelled factors over agricultural lands of India, China and Eastern Europe (Fig. 4c).

Nitrogen deposition

Anthropogenic changes in the amount, rate and distribution of nitrogen deposition can impact greening patterns, since insufficient nitrogen availability can stunt plant growth107,108,109, potentially slowing greening or causing browning, but excess nitrogen can enhance plant growth in nitrogen-limited systems109. However, the few DGVMs that include the nitrogen cycle do not indicate that nitrogen deposition plays a dominant driving role on the greening at either the global or regional scales (Fig. 4). Modelling studies differ on the contribution of increasing nitrogen deposition to the global LAI increase11 (9 ± 12%), largely due to the incomplete representation of nitrogen-related processes110. A growing number of DGVMs are currently incorporating nitrogen processes111, though, and future research priorities include better measurement and representation of processes such as plant nitrogen uptake and allocation110.

Impact of greening on the carbon cycle

Greening increases the amount of photosynthetically active sunlight that is absorbed by vegetation and, thus, enhances productivity112,113. There has been substantial evidence showing enhanced vegetation productivity from contiguous solar-induced fluorescence (CSIF; Fig. 1f) observations114, empirical models of vegetation productivity92,115 and DGVM and ESM simulations70,116 (Fig. 6). It should be noted, though, that CSIF is not fully independent from MODIS greenness indices, since its derivation relies on both solar-induced fluorescence measurements from Orbiting Carbon Observatory 2 and MODIS reflectance measurements114.
Fig. 6: Changes in global carbon fluxes and seasonal CO2 amplitude.
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Graphs depict changes in Barrow, AK, USA, since 1980. a | Global gross primary production (GPP). b | Net primary production. c | Net biome production (NBP). d | Residual land sink. e | Seasonal CO2 amplitude. The GPP is from the ensemble mean of 16 dynamic global vegetation models (DGVMs)111. The NPP is from greenness-based modelling by Smith et al.198. The NBP is from the ensemble mean of 16 DGVMs and two atmospheric inversions111. Residual land sink is the mass balance residual of anthropogenic CO2 emissions, the atmospheric CO2 growth rate and the ocean CO2 budget111. The shaded areas indicate the standard deviation of the GPP, NPP or NBP across models. The dashed lines indicate linear trends.
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Enhanced vegetation productivity increases terrestrial carbon storage, slowing down anthropogenic climate warming117. For example, about 29% of anthropogenic CO2 emissions since the 1980s have been offset by the land carbon sink (2.5 ± 1.0 PgC year−1)111. This vegetation-induced large land carbon sink was also inferred from forest inventories118 and above-ground biomass estimated from the vegetation optical depth (Fig. 1e), a microwave-based satellite measurement of both woody and leaf biomass119. Multiple lines of evidence, including analyses from DGVMs, atmospheric inversion models and the residual land sink (the mass balance residual of anthropogenic CO2 emissions, atmospheric CO2 growth rate and ocean CO2 budget), confirm the increasing magnitude of the global land carbon sink since the 1980s111 (Fig. 6). An ecosystem model driven by satellite LAI measurements estimated that increased LAI accounts for 36% (0.4 PgC year−1) of the land carbon sink enhancement of 1981–2016 (ref.112). Recent studies indicate that the trend in the land carbon sink has further accelerated since the late 1990s120,121. For example, the rate of update during 1998–2012 was three times that of 1980–1988 (0.17 PgC year−2 in comparison with 0.05 PgC year−2)121, attributed to afforestation-induced greening in the temperate Northern Hemisphere13,121. These hotspots of afforestation and forest regrowth are in accordance with the greening pattern observed since 2000 by MODIS (Fig. 2c). Recent DGVM studies122,123 have further confirmed that the carbon sink during the 2000s was partly driven by afforestation and forest regrowth in East Asia and Europe124. The extensive greening over croplands, however, has probably contributed less to the carbon sink, because only a minor portion of assimilated carbon by crops remain sequestered due to crop harvest. The impact of greening on the carbon cycle is also partly responsible for the increasing seasonality of atmospheric CO2 in the northern high latitudes125. The amplitude of the Northern Hemisphere CO2 seasonal cycle has increased by as much as 50% for latitudes north of 45°N126,127 since the 1960s, indicating enhanced vegetation productivity in northern ecosystems during the carbon-uptake period128. The spring zero-crossing date — the time when the detrended seasonal CO2 variations down-cross the zero line in spring — is a phenological indicator of the timing of early season net carbon uptake125,129. From 1987 to 2009, the spring zero-crossing date has advanced at high-latitude stations130 (from −0.5 days decade−1 to −1.8 days decade−1) (Fig. 3d), a trend that is consistent with the advancing SOS (Fig. 3b). At the end of the net carbon-uptake period, the autumn zero-crossing dates of detrended seasonal CO2 variations — the time when the detrended seasonal CO2 variations up-cross the zero line in autumn — have also advanced over eight of the ten Northern Hemisphere stations studied131. The observed autumn zero-crossing date advancement (Fig. 3d) is in contrast to the delayed EOS (Fig. 3a) in autumn. This divergence in the autumnal CO2 and greenness trends suggests that, unlike in spring, autumn vegetation greening does not lead to an increased carbon sink because respiration is increasing more quickly than photosynthesis in autumn131. Visual observations (for example, from the Pan European Phenology Project PEP725) and cameras (for example, PhenoCam datasets) are providing an increasing amount of ground-based phenological evidence of this process. In the future, these data can be paired with eddy covariance flux data, to further our mechanistic understanding of the climate-change-induced seasonal change in greenness and carbon balance.

Biogeophysical impacts of greening

Greening has discernable impacts on the hydrologic cycle and climate through modifying surface biogeophysical properties (for example, albedo, evapotranspiration (ET) and surface roughness) on local to regional and global scales19,132 (Fig. 7). Vegetation’s biogeophysical feedbacks to climate are, thus, critical to understanding the potential of ecosystem management, such as afforestation, for climate change mitigation3,132,133. In this section, we present the feedbacks of vegetation greening on the hydrologic cycle and land-surface air temperature.
Fig. 7: Biogeophysical feedbacks of recent vegetation greening to the climate system.
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a | Schematic diagram summarizing land-surface and atmospheric processes through which changes in vegetation greenness feed back into the climate system. For each process or flux, the corresponding symbols ‘−’, ‘+’ and ‘?’ in brackets represent an increasing, decreasing and unknown trend, respectively, in response to vegetation greening, and the colour of arrows represents impacts on water (blue) or energy balance (red, except the latent heat in blue). b | Summary of greening-induced changes in major global water cycle fluxes in mm year−1 from 1982 to 2011. Data courtesy of Zeng et al.19. c | Summary of greening-induced changes in global surface energy balance in W m−2 from 1982 to 2011. Data courtesy of Zeng et al.144. The error bars show the standard error of the estimates. The bar colours are the same as the corresponding fluxes shown in part a. ET, evapotranspiration.
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The hydrologic cycle

Vegetation greening modulates water cycling. Land water losses to the atmosphere occur through ET, which includes transpiration (60–90% of the total land ET134,135,136) and evaporation. Greening increases water losses through an extended area of leaves performing transpiration137. A larger foliage area reduces the bare ground surface from which soil evaporation occurs, but increases the re-evaporation of rainfall intercepted by leaves138, so that greening can cause the net evaporation to either increase or decrease. Various remote-sensing-based ET estimates consistently point to a significant increase in global terrestrial ET over the past four decades, suggesting an intensified water exchange between the land and the atmosphere concurrent with the greening trend139. More than half of the global ET increase since the 1980s has been attributed to vegetation greening138,139 (Fig. 7). By controlling the changes in ET, vegetation greening also alters the water distribution between regions and water pools (for example, water in soil, rivers and the atmosphere). Assuming that precipitation does not change in response to vegetation greening, a greening-induced ET increase will reduce soil moisture and runoff, which can intensify droughts at the catchment scale140,141. In China’s Loess Plateau for instance, where intensive afforestation is associated with a pronounced local greening, the river discharge has indeed decreased by a rate of 0.25 km3 year−2 over the past six decades142. However, when using ESMs that consider both the greening-induced ET increase and consequent changes in precipitation, simulations forced only with satellite-observed LAI trends do not generate dramatic changes in soil moisture or runoff at continental or global scales143,144. This is because greening-induced ET enhancement increases atmospheric water vapour content, which, in turn, promotes downwind precipitation145,146. The enhanced precipitation over transpiring regions is particularly evident in moist forests147 like the Amazon or Congo, which are ‘closed’ atmospheric systems where 80% of the rainfall originates from upwind ET145. Such an efficient atmospheric water recycling mitigates water loss from the soil, sustains inland vegetation and maintains mesic and humid ecosystems. In addition to intensifying water cycling at the annual scale, vegetation greening also induces seasonal hydrologic changes. There is emerging evidence that spring-greening-enhanced ET leads to a reduction in soil moisture content, which carries over into the following summer and likely suppresses vegetation growth and increases the risk of heatwaves148,149. The greening-induced water loss through ET is recycled as land precipitation in subsequent months, benefitting some remote regions through modulating large-scale atmospheric circulation patterns, despite often being insufficient to compensate for evaporative water loss locally149. Proposed climate-mitigation strategies, such as afforestation, therefore need to fully consider coupling between vegetation and other components of the Earth system.

Land-surface air temperatures

Greening impacts the exchange of energy between the land and the atmosphere, which ultimately leads to modifications in surface air temperature150. Greening increases ET, which cools the surface through evaporative cooling19,150, but greener canopies have a lower albedo than bare ground and absorb more sunlight, which can result in a larger sensible heat flux. This enhanced sensible heat warms the land surface, an effect called albedo warming151. The net effect of greening on surface air temperature in many cases can be viewed as the balance between evaporative cooling and albedo warming152,153, which was estimated globally to be −0.9 W m−2 from evaporative cooling and +0.1 W m−2 from albedo warming19 (Fig. 7c). Greening can also trigger a series of changes through atmospheric circulation that indirectly affect the surface temperature154. For example, the additionally transpired water enhances atmospheric water vapour content, which results in more longwave solar radiation entrapment and re-emission in the atmosphere, but reduces the amount of shortwave solar radiation reaching the Earth’s surface through increased cloud formation19,155,156 (Fig. 7). When all the aforementioned impacts of vegetation greening on near-surface air temperature were simulated in coupled ESMs driven by the satellite-based greening since the 1980s, the results suggested a net cooling trend by 12% ± 3% of the concurrent observed warming rate19. In warm regions such as the tropics and subtropics, evaporative cooling effects are generally larger than albedo warming effects, leading to a net cooling effect when vegetation greenness increases19,157,158. However, the net effect of greening on surface air temperature over the Northern Hemisphere extratropical regions is still subject to debate. Studies based on idealized afforestation and/or deforestation experiments1,159 or comparisons of the energy budget differences between paired forest and short vegetation sites132,153 suggested that the albedo warming effect plays a dominant role. These studies, though, assumed complete land cover changes, whereas greening can be gradual. By integrating satellite observations with ESMs, several studies provided an alternative approach that more realistically simulated the effects of vegetation greenness changes and isolated the signal of climate response to greening. These studies found that greening slowed down warming through evaporative cooling in Arctic and boreal regions19, the Tibetan Plateau160 and temperate regions like East Asia161. Nonetheless, current state-of-the-art modelling efforts are still inconclusive, as some processes are not yet well represented in ESMs, such as snow masking by greener canopies during cold seasons162,163,164 and the partitioning of transpiration and evaporation that is sensitive to vegetation greenness change136. Since most ESMs underestimate the ratio of transpiration to ET136, evaporative cooling by greening could have been underestimated19,133.

Conclusions

Widespread vegetation greening since the 1980s is one of the most notable characteristics of biosphere change in the Anthropocene. Greening has significantly enhanced the land carbon sink, intensified the hydrologic cycle and cooled the land surface at the global scale. A mechanistic understanding of the underlying drivers shows how anthropogenic forcing has fundamentally altered today’s Earth system through a set of feedback loops. Improved knowledge of greenness changes, together with recent progress in observing technology and modelling capacity, has resulted in major advances in understanding global vegetation dynamics. Nonetheless, we still face many challenges ahead. One key challenge is to continue developing the capacity of remote sensing to measure vegetation structure and functions. Although the vegetation greenness indices described in this Review have proved highly reliable, contemporary satellite greenness products still suffer from limitations, such as inadequate sensitivity to detect changes in dense vegetation, aliasing between snow cover decrease and leaf area increase in cold ecosystems (such as boreal forests), atmospheric contamination, orbital drift and sensor replacements. Compared with the AVHRR, the new moderate-resolution spectral bands and spatial resolutions of 250 m to 1 km of the MODIS sensors on board the Terra (operating since 1999) and Aqua (operating since 2002) satellites have provided global datasets that largely improved the long-term monitoring of vegetation greenness13. The current scientific community needs to include Earth observations with higher temporal, richer spectral and finer spatial resolutions to capture various ecosystem functions and processes responding to different parts of the electromagnetic spectrum165. We expect the development of next-generation satellite missions and vegetation indices to better fulfil these needs. For example, ongoing efforts on developing hyperspectral remote sensing such as the EnMAP, FLEX and HyspIRI missions will improve the richness and specificity of spectral information on vegetation structure and functioning. Another equally important challenge is to validate satellite-based greenness changes with ground observations. Currently, the lack of systematic long-term ground observations covering a large spatial gradient from the high Arctic to the tropics has led to few available ground truths166 to confirm greenness changes detected through satellite products. Therefore, expanding existing observational networks (such as PhenoCam and FLUXNET) is a high priority. For example, the mismatch between the spatial distribution of vegetation productivity and the density of FLUXNET sites167 highlights the need to expand the current network from the mid-latitudes to the tropics, where the most photosynthesis takes place. Also, growing crowd-sourced observations by citizen scientists, such as the CrowdCurio phenology observations over the eastern USA168, can provide valuable data that complement the more expensive professional ground observation networks. These increasing types and amounts of data, together with the rapid development of deep learning169 and process modelling11, offer promising tools for improving our understanding of vegetation greening169. Considerable uncertainties remain in ESM projections on if and where vegetation greening will occur. Recent studies have identified several processes causing vegetation browning in some regions, including forest diebacks170, insect35 and disease outbreaks171, thermokarst development172, human mismanagement36,173, destructive logging174 and industrial development175. These emerging threats could lead to unexpected changes in vegetation greenness relative to our current projections (such as the projections shown in Figs 2e–h,5), since these processes are under-represented in ESMs. Thus, integrating continued space and ground monitoring and advancing ESM developments is a critical cross-sectoral research priority.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (41861134036, 41988101) and the Research Council of Norway (287402), the National Key R&D Program of China (2017YFA0604702), Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0208) and the Thousand Youth Talents Plan project in China. The works of C.C., R.B.M. and T.P. were funded by NASA’s Earth Science Division. R.B.M. also acknowledges support by the Alexander von Humboldt Foundation, Germany. P.C. acknowledges support by the European Research Council Synergy project (SyG- 2013-610028 IMBALANCE-P) and the ANR CLAND Convergence Institute. The authors thank Z. Zhu, Y. Li, K. Wang, Y. Deng, M. Gao and X. Li for their help in preparing the manuscript.

Author information

Affiliations

  1. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
    • Shilong Piao
    • , Xuhui Wang
    • , Xu Lian
    • , Yue He
    •  & Philippe Ciais
  2. Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
    • Shilong Piao
  3. Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China
    • Shilong Piao
  4. Department of Earth and Environment, Boston University, Boston, MA, USA
    • Taejin Park
    • , Chi Chen
    •  & Ranga B. Myneni
  5. NASA Ames Research Center, Moffett Field, CA, USA
    • Taejin Park
    •  & Ramakrishna R. Nemani
  6. Norwegian Institute for Nature Research, FRAM – High North Research Centre for Climate and the Environment, Tromsø, Norway
    • Jarle W. Bjerke
    •  & Hans Tømmervik
  7. Department of Biology, Colorado State University, Fort Collins, CO, USA
    • Anping Chen
  8. Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France
    • Philippe Ciais
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