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Characteristics, drivers and feedbacks of global greening

Authors:
  • NASA Ames Research Center / Bay Area Environmental Research Institute

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.
Vegetation controls the exchange of carbon, water,
momentum and energy between the land and the atmos-
phere, and provides food, fibre, fuel and other valuable
ecosystem services1,2. Changes in vegetation structure
and function are driven by climatic and environmen-
tal changes, and by human activities such as land- use
change. Given that increased carbon storage in vegeta-
tion, 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 obser-
vations that can detect these changes both spatially and
temporally. While monitoring the changes in some vege-
tation 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 green-
ness) (BOX1) revealed a close connection between vege-
tation canopy greenness and photosynthesis acti vity
(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 globe813. This ‘vegetation greening’
is defined as a statistically signi ficant 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 under-
standing 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 vege-
tation 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 driv-
ing vegetation greening at the global scale. Subsequent
studies based on fine- resolution and medium- resolution
satel lite data13 have shown the critical role of land- surface
history, including afforestation and agricultural intensi-
fication, 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
Afforestation
The conversion of treeless
lands to forests through
planting trees.
Characteristics, drivers and feedbacks
of global greening
ShilongPiao
1,2,3*, XuhuiWang1, TaejinPark4,5, ChiChen
4, XuLian1, YueHe1,
JarleW.Bjerke6, AnpingChen
7, PhilippeCiais
1,8, HansTømmervik6,
RamakrishnaR.Nemani5 and RangaB.Myneni4
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.
*e- mail: slpiao@pku.edu.cn
https://doi.org/10.1038/
s43017-019-0001-x
Reviews
Nature reviews
|
Earth & EnvironmEnt
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 vegeta-
tion 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 inter-
pretation and is a fundamental variable in almost all
land- surface models (BOX1). An ensemble of LAI data-
sets 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 process-
ing2224. For example, using a revised calibration of the
MODIS data in the most recent collection 6 (C6) data-
set24, 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.
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 increas-
ing 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.2ad). 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 photo-
graphic evidence further corroborate these findings2730.
In general, the LAI over high northern latitudes will con-
tinue to increase by the end of this century31, based on
the results of an ensemble of ESMs (FIG.2eh). 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 multi-
tude of disturbances (for example, fires, harvesting and
insect defoliation) prevail9,3337. The North American
boreal forests in particular exhibit browning areas nearly
20 times larger than the Eurasian boreal forests, showing
heterogeneous regional greenness change38.
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
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.
Author addresses
1Sino-FrenchInstituteforEarthSystemScience,CollegeofUrbanandEnvironmental
Sciences,PekingUniversity,Beijing,China.
2KeyLaboratoryofAlpineEcology,InstituteofTibetanPlateauResearch,Chinese
AcademyofSciences,Beijing,China.
3CenterforExcellenceinTibetanEarthScience,ChineseAcademyofSciences,
Beijing,China.
4DepartmentofEarthandEnvironment,BostonUniversity,Boston,MA,USA.
5NASAAmesResearchCenter,MoffettField,CA,USA.
6NorwegianInstituteforNatureResearch,FRAM–HighNorthResearchCentre
forClimateandtheEnvironment,Tromsø,Norway.
7DepartmentofBiology,ColoradoStateUniversity,FortCollins,CO,USA.
8LaboratoiredesSciencesduClimatetdel’Environnement,CEACNRSUVSQ,
Gif-sur-Yvette,France.
www.nature.com/natrevearthenviron
Reviews
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 tropi-
cal vegetation greenness estimations due to the saturation
effects of greenness indices in dense vegetation41 and con-
tamination by clouds and aerosols42. These uncertainties
partly underlie the disagreement between the MODIS
and AVHRR products13 when measuring tropical green-
ness and the debate on whether the Amazonian forests
have greened or browned in response to droughts4244.
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.2ad). Regional greening
hotspots in southern Brazil and southeast Australia
mostly overlap with the intensive cropping areas13, high-
lighting the increasing contribution of managed eco-
systems to vegetation greening. Note that most of this
region is dominated by semi- arid ecosystems46, where
vegetation coverage is generally sparse. Thus, satel-
litevegeta tion indices over this region are generally sensi-
tive 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 temper-
ate and high latitudes, greenness often shows distinctive
seasonal patterns within a calendar year (FIG.3). Several
metrics of land- surface phenology have been developed
Land- surface phenology
Cyclic phenomena in vegetated
land surfaces observed from
remote sensing.
Box 1 | Remotely sensed vegetation greenness
Remotelysensedvegetationgreennessgenerallyrefers
tospectralvegetationindices(VIs)ortheleafarea
index(LAI).Photosyntheticpigmentsinplantleaves
(mainlychlorophyllandcarotenoids)stronglyabsorb
photosyntheticallyactiveradiation,whichlargely
overlapswiththevisiblespectrum(400–700nm),
particularlyredwavelengths(620–700nm).
Inthenear-infrared(NIR)domain(700–1,300nm),
absorbancebyleafconstituentsiseithersmall
orabsent;thus,scatteringincreasesthelikelihood
thatphotonswillexittheleaf.Thisisthebiophysical
basisforhighleaf-levelreflectanceintheNIRregion.
Atthecanopyscale,structuralpropertiessuchas
LAIandleaf-angledistributiondominatevariability
inNIRreflectance176.Thisuniquespectralsignature
ofvegetationintheredandNIRchannels,
acharacteristicnotpresentincommon
non-vegetativefeaturessuchassoil,snow
andwater177,178,hasthusbeenutilizedto
derivenumericalVIsmeasuring
vegetationgreenness176,179,180
(SupplementaryTableS1).
Forexample,thenormalized
differencevegetationindex,
whichisoneofthemostwidelyused
VIsinassessingvegetationgreenness
anditschangesfromlocaltoglobalscales
(SupplementaryTableS2),isusefulfor
measuringcanopystructuralproperties,such
asleafarea,lightinterceptionandbiomass41,181,182.
Satellitesensors,suchastheAdvancedVery-High-
ResolutionRadiometer(AVHRR),ModerateResolution
ImagingSpectroradiometer(MODIS),Vegetation,MediumResolutionImagingSpectrometer(MERIS)andVisible
InfraredImagingRadiometerSuite(VIIRS),havebeendeployedwithvaryingtemporalcoverage,providingVIproducts
basedonawiderangeofspectral-bandspecificationsanddataprocessing(SupplementaryTableS3).Forexample,
theAVHRRdoesnothaveabluechannel,sothissensorisunabletoproduceblue-band-basedgreennessindices
liketheenhancedvegetationindex.Thesesensordifferencesmakeitanon-trivialchallengetoproduceconsistent
andcontinuinglong-termgreennessproducts183.
ComparedwithVIs,theLAI(theone-sidedgreenleafareaperunitgroundareainbroadleafcanopiesorone-half
ofthetotalneedlesurfaceareaperunitgroundareainconiferouscanopies184,185)isawell-definedphysicalattributeof
vegetation.TheLAIisastatevariableinalllandmodelsandkeytoquantifyingtheexchangesofmass,momentumand
energybetweenthesurfaceandtheatmosphere.MultipleapproachesforretrievingtheLAIfromremotesensingdata
havebeendeveloped—thesecanbeconceptuallycategorizedas:empiricalapproachesthatarebasedonrelationships
betweenVIsandtheLAI186,187;machine-learningapproachesthattrainsurfacereflectanceorVIstogivenreference
LAIs182,188,189;andphysicalapproachesthatarebasedonthephysicsofradiationinteractionwithelementsofacanopyand
transportwithinthevegetativemedium184,190,191.SeeSupplementaryTableS4forcurrentlyavailableglobalLAIproducts.
AVHRR (1981–)
Vegetation (SPOT) (1998–2014)
Vegetation (PROBA) (2013–)
MODIS (2000–)
MERIS (2002–2012)
VIIRS (2012–)
1.0
0.8
0.6
0.4
0.2
0.0
400
500
600
700
800
900
1,000
1,100
Wavelength (nm)
Reflectance
Blue Red Near infrared
Vegetation
Soil
Snow
Water
Nature reviews
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Earth & EnvironmEnt
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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 phenol-
ogy dates can vary depending on the greenness product
or algorithm used4951, 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 sea-
son (LOS) (2–10 days decade−1), have been observed in
most Northern Hemisphere regions during the past four
decades7,8,25,5254 (FIG.3ac). These trends are corroborated
by ground-based observation data in spring and
autumn5557. 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 Eurasia25,5860.
In addition to longer growing seasons, satellite green-
ness 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 signif-
icantly change the Earth’s seasonal landscape. Northern
high latitudes, which traditionally have high seasonality
(that is, short and intense growing seasons), are exhibit-
ing seasonality similar to that of their counterparts
6° to 7° south in the 1980s. In other words, the latitudi-
nal 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 k m 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 green-
ing, 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 contrib-
ution 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 quanti-
tatively attribute the observed greening trend to each of
these factors.
CO2 fertilization. As CO2 is the substrate for photo-
synthesis, rising atmospheric CO2 concentration can
enhance photosynthesis66 by accelerating the rate of
carboxylation; this process is known as the ‘CO2 ferti-
lization effect’. In addition, increased CO2 concentra-
tions 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) ensem-
ble of dynamic global vegetation models (DGVMs)70
suggests that rising CO2 is the dominant driver of veg-
etation greening, accounting for nearly 70% of global
a
b
c
d
e
f
GIMMS LAI3g
MODIS C6
GLASS v4.0
GLOBMAP v3.0
–0.1
0
0.1
0.2
0.3
LAI anomaly (m2 m–2)
–0.02
–0.01
0
0.01
0.02
NDVI anomaly
–10
–5
0
5
10
–10
–5
0
5
10
EVI
anomaly (10–3)
NIRv
anomaly (10–3)
–10
–5
0
5
10
–10
–5
0
5
10
VOD
anomaly (10–3)
1980
CSIF anomaly
(10–3 mW m–2 nm–1 sr–1)
1985 1990 1995 2000 2005 2010 2015 2020
GIMMS MODIS C6 SPOT
Fig. 1 | Changes in satellite- derived global vegetation indices, vegetation optical
depth and contiguous solar- induced fluorescence. a | Leaf area index (L AI) from
four products: GIMMS13, GL ASS192, 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 L AI 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.
Carboxylation
The addition of CO2 to
ribulose 1,5-bisphosphate
during photosynthesis.
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a
g
h
e
cd
b
<–12 –8 –4 –2 –1 012357912 >18
Trends in GS mean LAI (10–3 m2 m–2 year–1)
Trends in GS mean LAI (10–3 m2 m–2 year–1)
12357
<–12 –8 –4 –2 –1 0912 >18
<–25–20 –15 –10 –5 0510 15 20 25 30 50 >70
Predicted changes in GS mean LAI (%)
f
<–25–20 –15 –10 -5 0510 15 20 25 30 50 >70
Predicted changes in GS mean LAI (%)
2020 2040 2060 2080 2100
2020 2040 2060 2080
2100
0
10
20
30
40
50
60
-5
0
5
10
15
20
25
30
GS mean LAI anomaly (m2 m–2)
GS mean LAI anomaly (m2 m–2)
Predicted changes in GS
mean LAI (%)
1982–2009 (GIMMS, GLOBMAP and GLASS mean)
2000–2018 (MODIS)
2081–2100 relative to 1981–2000, RCP2.6
2081–2100 relative to 1981–2000, RCP8.5
–0.4
–0.3
–0.2
–0.1
0
0.1
0.2
0.3
0.4
1985 1990 1995 2000 2005 2010
2000 2005 2010 2015 2020
–0.12
–0.08
–0.04
0
0.04
0.08
0.12
High-latitude NH
Temperate NH
Tropical
Extratropical SH
Fig. 2 | Spatial patterns of changes in leaf area index. a | Growing season (GS) mean Advanced Very- High-Resolution
Radiometer (AVHRR) leaf area index (L AI) trend during 1982–2009. The AVHRR L AI dataset is the average of three
different products (GIMMS13, GLOBMAP23 and GL ASS192). b | Change in the GS mean AVHRR L AI over four regions during
1982–2009. c | GS mean Moderate Resolution Imaging Spectroradiometer (MODIS) L AI during 2000–2018. d | Change
in the GS mean MODIS L AI over four regions during 2000–2018. MODIS L AI is from collection 6 (REF.193). e | Relative
change in GS mean L AI 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 L AI 2018–2100 under RCP2.6, relative to 1981–2000. g | Relative change in GS mean L AI
between 1981–2000 and 2081–2100 under RCP8.5, based on CMIP5. h | Relative change in GS mean CMIP5 L AI 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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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 enhance-
ment75, 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 eco-
systems11,77,78 (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2eh,5) might overestimate the CO2
fertilization effects.
Climate change. Although rising atmospheric CO2 con-
centration is the main driver of global greening, climate
change, such as anthropogenic warming and regional
trends in precipitation, is a dominant driver of green-
ness changes over 28% of the global vegetated area11.
The global contribution of climate change to increas-
ing 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 tem-
perature is close to vegetation optimal temperature85,
but warming significantly increases vegetation green-
ness 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 anthropo-
genic warming on greenness appears to have weakened
during the past four decades90,91, when the correla-
tion 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 satel-
lite measurements93,94, was primarily driven by increases
in precipitation after a severe drought in the early
1980s9698. 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 affores-
tation increases forest area over temperate regions,
0.1
0.2
0.3
0.4
0.5
0.6
NDVI
a
–60 –40 –20 0204060
Change in SOS (days)
0
2
4
6
8
Frequency (104)
b
–60 –40 –20 02040
60
Change in EOS (days)
60 120 180 240 300
Day of year
–15
–10
–5
0
5
10
Detrended CO2 (ppm)
d
SOS EOS
Peak
1982–1986
>50°N
2008–2012
1980–1984
Barrow (71°N)
2013–2017
LOS
60 120 180 240 300
Day of year
0
2
4
6
Frequency (104)
c
DelayAdvance DelayAdvance
Seasonal
amplitude
SZC
AZC
Peak
Trough
Fig. 3 | Changes in the seasonality of vegetation greenness and atmospheric CO2
concentration. 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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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 addi-
tion, agricultural intensification with multiple cropping,
irrigation and fertilizer usage must contribute consider-
ably 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 growth107109, potentially slow-
ing 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 domi-
nant 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 incom-
plete representation of nitrogen- related processes110.
Agrowing number of DGVMs are currently incorporat-
ing nitrogen processes111, though, and future research pri-
orities include better measurement and representation of
processes such as plant nitrogen uptake and allocation110.
a
c
b
–CO2 –CLI –NDE –LCC –OF +OF +LCC +NDE +CLI +CO2
GIMMS
GLASS
GLOBMAP
CLM4.5
LPX-Bern
OCN
LPJ
LPJ-GUESS
ORCHIDEE
VISIT
CLM4
CABLE
VEGAS
Trends in LAI (m2 m–2 year–1)
–0.04
–0.02
0
0.02
0.04
0.06
0.08
0.10
0.12
>50°
N>
25°S25°–
50°N
25°S–
25°N
–0.03
0
0.03
0.06
0.09
0.12
0.15
Trends in LAI (m2 m–2 year–1)
OBS CO2CLI NDE LCC
Fig. 4 | Attribution of trends in growing season mean leaf area index. a | Trends in the global- averaged leaf area
index (L AI) 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 L AI trends are shown as coloured dots. b | Contribution of different drivers to L AI 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
L AI trend, defined as the driver that contributes the most to the increase (or decrease) in L AI in each vegetated grid
cell. Other factors (OF) is defined by the fraction of the observed L AI 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 L AI 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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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.
Enhanced vegetation productivity increases terres-
trial carbon storage, slowing down anthropogenic
climate warming117. For example, about 29% of anthro-
pogenic 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 evi-
dence, including analyses from DGVMs, atmospheric
inversion models and the residual land sink (the mass
balance residual of anthropogenic CO2 emissions, atmos-
pheric 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 fur-
ther 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 stud-
ies122,123 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 contrib-
uted less to the carbon sink, because only a minor por-
tion 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 vari-
ations down- cross the zero line in spring — is a pheno-
logical 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 stud-
ied131. The observed autumn zero- crossing date advance-
ment (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 car-
bon 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 covari ance 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 biogeo-
physical properties (for example, albedo, evapotranspir-
ation (ET) and surface roughness) on local to regional
and global scales19,132 (FIG.7). Vegetations biogeophysi-
cal feedbacks to climate are, thus, critical to under-
standing the potential of ecosystem management, such
as afforestation, for climate change mitigation3,132,133.
In this section, we present the feedbacks of vegetation
Evapotranspiration
The flux of water emitted
from the Earth’s surface to
the atmosphere. It is the sum
of evaporation by the soil,
wet canopy, open- water
surfaces and transpiration
by plant stomata.
0
0.2
0.4
0.6
LAI anomaly (m2 m–2)
Observed
2000–2018
CMIP5 2081–2100
RCP6.0
RCP2.6
Observed 2000–2018
RCP8.5
RCP4.5
Fig. 5 | Current and predicted global leaf area index.
Current (observed 2000–2018) leaf area index (L AI)
anomaly (m2 m2) from an average of satellite measure ments
based on GIMMS13, GL ASS192, GLOBMAP23 and Moderate
Resolution Imaging Spectroradiometer (MODIS) C6
(REF.193). Predicted L AI 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 L AI of
CMIP5 models; the black and white lines indicate the
mean and median L AI of CMIP5 models, respectively.
L AI anomalies were calculated against the average
during 1980–2005.
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greening on the hydrologic cycle and land- surface
air temperature.
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 ET134136) 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 evapo-
ration 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 concur-
rent 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 green-
ing 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 pronoun-
ced 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 precipita-
tion145,146. The enhanced precipitation over transpiring
regions is parti cularly 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 recy-
cling 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
hydro logic changes. There is emerging evidence that
spring- greening-enhanced ET leads to a reduction in
soil moisture content, which carries over into the fol-
lowing summer and likely suppresses vegetation growth
and increases the risk of heatwaves148,149. The greening-
induced water loss through ET is recycled as land precip-
itation 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, there-
fore 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 atmos-
phere, which ultimately leads to modifications in sur-
face 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.
-2
1980 1985 1990 1995 2000 2005 2010 2015
CO
2
amplitude
anomaly (ppm)
–10
–5
0
5
10
a
b
c
d
e
–2
–1
0
1
2
–2
–1
0
1
2
–2
–3
–1
0
1
2
–2
–3
–1
0
1
2
3
GIMMS NPP anomaly
(PgC year–1)
DGVMs NBP anomaly
(PgC year–1)
C flux anomaly
(PgC year–1)
DGVMs GPP anomaly
(PgC year–1)
Trend = 0.47 PgC year–2 , P 0.001
Trend = 0.05 PgC year–2 , P 0.001
Trend = 0.05 PgC year–2 , P 0.001
Trend = 0.08 ppm year–1 , P 0.001
Trend = 0.06 PgC year–2 ,
P 0.001
Trend = 0.06 PgC year–2 ,
P 0.001
Residual land sink
Inversion NBP
Fig. 6 | Changes in global carbon fluxes and seasonal CO2 amplitude. 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.
Transpiration
The loss of water from plants
to the atmosphere.
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The net effect of greening on surface air temperature in
many cases can be viewed as the balance between evap-
orative 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 entrap-
ment and re- emission in the atmosphere, but reduces the
amount of shortwave solar radiation reaching the Earths
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 affores-
tation and/or deforestation experiments1,159 or compar-
isons 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 satel-
lite 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 stud-
ies found that greening slowed down warming through
Shortwave
solar radiation
(–) Longwave
solar radiation
(+)
ET (latent heat)
evaporative cooling (+)
Aerodynamic
roughness (+)
Soil moisture (?) Soil moisture (?)
Low albedo (–)
Moderate albedo (–)
Reflected
solar radiation (–)
High albedo
Snow masking effects
Aerodynamic
roughness (+)
Runoff (?)
Soil
evaporation (–)
Sensible
heat (–)
Sensible
heat (–)
Latent
heat (+)
Interception (+)
Transpiration (+)
Clouds
(+)
Large-scale moisture transport
Moisture to downwind areas (+)
b Global water cycle (mm year–1)
a
Precipitation
Runoff
Soil moisture
Albedo
Latent heat
Aerodynamic resistance
Net radiative
forcing
Shortwave downward
radiation
Longwave downward
radiation
–12
–1.5
–1.2
–0.9
–0.6
–0.3
0.3
0.6
0
–9 –6 –3 036912 15
ET
c Global energy balance (W m
–2)
Precipitation from oceans
or upwind areas (?)
Precipitation from
local recycling (+) Precipitation (+)
Diffuse
solar radiation
(+)
Fig. 7 | Biogeophysical feedbacks of recent vegetation greening to the climate system. 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 year1 from 1982 to 2011. Data courtesy of Zeng etal.19. c | Summary of greening-
induced changes in global surface energy balance in W m2 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.
www.nature.com/natrevearthenviron
Reviews
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 seasons162164 and the partitioning
of transpiration and evaporation that is sensitive to veg-
etation greenness change136. Since most ESMs under-
estimate 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 mecha-
nistic 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model-
ling capacity, has resulted in major advances in under-
standing global vegetation dynamics. Nonetheless, we
still face many challenges ahead.
One key challenge is to continue developing the
capa city of remote sensing to measure vegetation struc-
ture 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 eco-
systems (such as boreal forests), atmospheric contami-
nation, 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 observ-
ations with higher temporal, richer spectral and finer
spatial resolutions to capture various ecosystem func-
tions and processes responding to different parts of the
electro magnetic spectrum165. We expect the develop-
ment 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 observ-
ations. 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 avail-
able ground truths166 to confirm greenness changes
detected through satellite products. Therefore, expand-
ing existing observational networks (such as PhenoCam
and FLUXNET) is a high priority. For example, the mis-
match between the spatial distribution of vegetation
productivity andthe density of FLUXNET sites167 high-
lights the need to expand the current network from the
mid-latitudes to the tropics, where the most photosyn-
thesis takes place. Also, growing crowd- sourced obser-
vations by citizen scientists, such as the CrowdCurio
phenology observations over the eastern USA168, can
provide valuable data that complement the more expen-
sive 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 projec-
tions 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, destruc-
tive 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2eh,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 pro-
ject 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 contributions
S.P., X.W., T.P., C.C., X.L., Y.H., J.W.B., A.C., P.C., H.T. and
R.B.M. wrote the first draft of the manuscript. S.P., X.W.
and R.B.M. reviewed and edited the manuscript before sub-
mission. All authors made substantial contributions to the
discussion of content.
Competing interests
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
Supplementary information
Supplementary information is available for this paper at
https://doi.org/10.1038/s43017-019-0001-x.
© Springer Nature Limited 2019
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Vegetation restoration has brought about remarkable landscape evolution in arid regions, and it is of great significance to evaluate its ecological benefits. However, the landscape evolution and ecological effects of different vegetation restoration measures have yet to be distinguished, and their future trends remain to be revealed, especially from the perspective of fine vegetation classification. In this study, we evaluated the ecosystem service value (ESV) of the northern sand prevention belt (NSPB) based on the fine land use/cover classification and benefit transfer method. Then, we analyzed changes in landscape and ESV induced by vegetation restoration from 2000 to 2015 and designed 9 future vegetation restoration scenarios to improve ESV. The results showed that the built-up area and dry farmland expanded by 35.37% and 3.48%, respectively, and paddy field and bush decreased by 19.00% and 6.80% from 2000 to 2015, respectively. Total ESV decreased by 0.62% (1.76 billion USD) during 2000–2015. The reduction in bush led to a loss of 0.85% (2.41 billion USD) in ESV. Vegetation restoration involved grassland restoration (1.99 million ha), bush restoration (0.26 million ha), and forestland restoration (0.18 million ha), which increased ESV by 1.21, 1.17, and 4.29 billion USD, respectively. Anthropogenic disturbance resulted in the loss of 2.97 million ha of vegetation and 9.23 billion USD in ESV. Current vegetation restoration in the NSPB is insufficient to counteract the effects of anthropogenic disturbance on ESV. Riparian vegetation buffer and grain for green scenarios have limited ecological benefits. Desertification control scenarios have the potential to increase ESV by at least 8.94% (25.12 billion USD) and to reverse ESV losses. Bush and grassland can be used instead of forestland for restoration in arid regions. This study can provide important support for the formulation and adjustment of landscape restoration in arid regions.
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Many studies have reported that urbanization leads to a decrease in the normalized difference vegetation index (NDVI) due to the expansion of impervious cover. Some studies, however, have reported positive NDVI trends in urban areas due to warming and CO2 fertilization effects, as well as the creation of green space. Thus, we examined spatial and temporal variations of the growing season maximum NDVI in a megacity, Seoul, which has rapidly urbanized over the past decades, by analyzing a 32-year time series (1987 – 2018) of Landsat satellite images in Google Earth Engine. Continuous change detection and classification and random forest algorithms were integrated to classify Seoul land cover types annually. We found an overall increasing NDVI trend at the city scale (0.002 yr⁻¹). Significant NDVI trends were found for approximately 46 % of Seoul, with greening and browning trends accounting for 39 % and 7 %, respectively. Greening pixels appeared mainly on impervious (23 % with a significant NDVI trend), deciduous (10 %), and evergreen (3 %) land cover as of 2018. Stable impervious, deciduous, and evergreen land cover pixels showed a greening trend over the 32 years (0.002 yr⁻¹), which stemmed from the planting of trees in areas with impervious cover, such as streets and residential areas, and vegetation growth in forest areas. Disturbed area pixels showed fluctuating NDVI values, but there were more greening pixels (20 %) than browning pixels (5 %). Our findings indicate that a detailed knowledge of land use and land cover changes is required to understand NDVI trends in urban areas.
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Framing and Context of the Report Chapter 1 Executive Summary This special report assesses new knowledge since the IPCC 5th Assessment Report (AR5) and the Special Report on Global Warming of 1.5oC (SR15) on how the ocean and cryosphere have and are expected to change with ongoing global warming, the risks and opportunities these changes bring to ecosystems and people, and mitigation, adaptation and governance options for reducing future risks. Chapter 1 provides context on the importance of the ocean and cryosphere, and the framework for the assessments in subsequent chapters of the report. All people on Earth depend directly or indirectly on the ocean and cryosphere. The fundamental roles of the ocean and cryosphere in the Earth system include the uptake and redistribution of anthropogenic carbon dioxide and heat by the ocean, as well as their crucial involvement of in the hydrological cycle. The cryosphere also amplifies climate changes through snow, ice and permafrost feedbacks. Services provided to people by the ocean and/or cryosphere include food and freshwater, renewable energy, health and wellbeing, cultural values, trade and transport. {1.1, 1.2, 1.5} Sustainable development is at risk from emerging and intensifying ocean and cryosphere changes. Ocean and cryosphere changes interact with each of the United Nations Sustainable Development Goals (SDGs). Progress on climate action (SDG 13) would reduce risks to aspects of sustainable development that are fundamentally linked to the ocean and cryosphere and the services they provide (high confidence1). Progress on achieving the SDGs can contribute to reducing the exposure or vulnerabilities of people and communities to the risks of ocean and cryosphere change (medium confidence). {1.1} Communities living in close connection with polar, mountain, and coastal environments are particularly exposed to the current and future hazards of ocean and cryosphere change. Coasts are home to approximately 28% of the global population, including around 11% living on land less than 10 m above sea level. Almost 10% of the global population lives in the Arctic or high mountain regions. People in these regions face the greatest exposure to ocean and cryosphere change, and poor and marginalised people here are particularly vulnerable to climate-related hazards and risks (very high confidence). The adaptive capacity of people, communities and nations is shaped by social, political, cultural, economic, technological, institutional, geographical and demographic factors. {1.1, 1.5, 1.6, Cross-Chapter Box 2 in Chapter 1} Ocean and cryosphere changes are pervasive and observed from high mountains, to the polar regions, to coasts, and into the deep ocean. AR5 assessed that the ocean is warming (0 to 700 m: virtually certain2; 700 to 2,000 m: likely), sea level is rising (high confidence), and ocean acidity is increasing (high confidence). Most glaciers are shrinking (high confidence), the Greenland and Antarctic ice sheets are losing mass (high confidence), sea ice extent in the Arctic is decreasing (very high confidence), Northern Hemisphere snow cover is decreasing (very high confidence), and permafrost temperatures are increasing (high confidence). Improvements since AR5 in observation systems, techniques, reconstructions and model developments, have advanced scientific characterisation and understanding of ocean and cryosphere change, including in previously identified areas of concern such as ice sheets and Atlantic Meridional Overturning Circulation (AMOC). {1.1, 1.4, 1.8.1} Evidence and understanding of the human causes of climate warming, and of associated ocean and cryosphere changes, has increased over the past 30 years of IPCC assessments (very high confidence). Human activities are estimated to have caused approximately 1.0oC of global warming above pre-industrial levels (SR15). Areas of concern in earlier IPCC reports, such as the expected acceleration of sea level rise, are now observed (high confidence). Evidence for expected slow-down of AMOC is emerging in sustained observations and from long-term palaeoclimate reconstructions (medium confidence), and may be related with anthropogenic forcing according to model simulations, although this remains to be properly attributed. Significant sea level rise contributions from Antarctic ice sheet mass loss (very high confidence), which earlier reports did not expect to manifest this century, are already being observed. {1.1, 1.4} Ocean and cryosphere changes and risks by the end-of-century (2081–2100) will be larger under high greenhouse gas emission scenarios, compared with low emission scenarios (very high confidence). Projections and assessments of future climate, ocean and cryosphere changes in the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) are commonly based on coordinated climate model experiments from the Coupled Model Intercomparison Project Phase 5 (CMIP5) forced with Representative Concentration Pathways (RCPs) of future radiative forcing. Current emissions continue to grow at a rate consistent with a high emission future without effective climate change mitigation policies (referred to as RCP8.5). The SROCC assessment contrasts this high greenhouse gas emission future with a low greenhouse gas emission, high mitigation future (referred to as RCP2.6) that gives a two in three chance of limiting warming by the end of the century to less than 2oC above pre-industrial. {Cross-Chapter Box 1 in Chapter 1} 1 1 2 In this report, the following summary terms are used to describe the available evidence: limited, medium, or robust; and for the degree of agreement: low, medium or high. A level of confidence is expressed using five qualifiers: very low, low, medium, high and very high, and typeset in italics, for example, medium confidence. For a given evidence and agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of agreement are correlated with increasing confidence (see Section 1.9.2 and Figure 1.4 for more details). In this report, the following terms have been used to indicate the assessed likelihood of an outcome or a result: Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as likely as not 33–66%, Unlikely 0–33%, Very unlikely 0–10%, and Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%, More likely than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed likelihood is typeset in italics, for example, very likely (see Section 1.9.2 and Figure 1.4 for more details). This Report also uses the term ‘likely range’ to indicate that the assessed likelihood of an outcome lies within the 17–83% probability range. 75 1 Characteristics of ocean and cryosphere change include thresholds of abrupt change, long-term changes that cannot be avoided, and irreversibility (high confidence). Ocean warming, acidification and deoxygenation, ice sheet and glacier mass loss, and permafrost degradation are expected to be irreversible on time scales relevant to human societies and ecosystems. Long response times of decades to millennia mean that the ocean and cryosphere are committed to long-term change even after atmospheric greenhouse gas concentrations and radiative forcing stabilise (high confidence). Ice-melt or the thawing of permafrost involve thresholds (state changes) that allow for abrupt, nonlinear responses to ongoing climate warming (high confidence). These characteristics of ocean and cryosphere change pose risks and challenges to adaptation. {1.1, Box 1.1, 1.3} Societies will be exposed, and challenged to adapt, to changes in the ocean and cryosphere even if current and future efforts to reduce greenhouse gas emissions keep global warming well below 2oC (very high confidence). Ocean and cryosphere-related mitigation and adaptation measures include options that address the causes of climate change, support biological and ecological adaptation, or enhance societal adaptation. Most ocean-based local mitigation and adaptation measures have limited effectiveness to mitigate climate change and reduce its consequences at the global scale, but are useful to implement because they address local risks, often have co-benefits such as biodiversity conservation, and have few adverse side effects. Effective mitigation at a global scale will reduce the need and cost of adaptation, and reduce the risks of surpassing limits to adaptation. Ocean-based carbon dioxide removal at the global scale has potentially large negative ecosystem consequences. {1.6.1, 1.6.2, Cross-Chapter Box 2 in Chapter 1} The scale and cross-boundary dimensions of changes in the ocean and cryosphere challenge the ability of communities, cultures and nations to respond effectively within existing governance frameworks (high confidence). Profound economic and institutional transformations are needed if climate-resilient development is to be achieved (high confidence). Changes in the ocean and cryosphere, the ecosystem services that they provide, the drivers of those changes, and the risks to marine, coastal, polar and mountain ecosystems, occur on spatial and temporal scales that may not align within existing governance structures and practices (medium confidence). This report highlights the requirements for transformative governance, international and transboundary cooperation, and greater empowerment of local communities in the governance of the ocean, coasts, and cryosphere in a changing climate. {1.5, 1.7, Cross-Chapter Box 2 in Chapter 1, Cross-Chapter Box 3 in Chapter 1} Robust assessments of ocean and cryosphere change, and the development of context-specific governance and response options, depend on utilising and strengthening all available knowledge systems (high confidence). Scientific knowledge from observations, models and syntheses provides global to local scale understandings of climate change (very high confidence). Indigenous knowledge (IK) and local knowledge (LK) provide context-specific and socio-culturally relevant understandings for effective responses and policies (medium confidence). Education and climate literacy enable climate action and adaptation (high confidence). {1.8, Cross-Chapter Box 4 in Chapter 1} Long-term sustained observations and continued modelling are critical for detecting, understanding and predicting ocean and cryosphere change, providing the knowledge to inform risk assessments and adaptation planning (high confidence). Knowledge gaps exist in scientific knowledge for important regions, parameters and processes of ocean and cryosphere change, including for physically plausible, high impact changes like high end sea level rise scenarios that would be costly if realised without effective adaptation planning and even then may exceed limits to adaptation. Means such as expert judgement, scenario building, and invoking multiple lines of evidence enable comprehensive risk assessments even in cases of uncertain future ocean and cryosphere changes. {1.8.1, 1.9.2; Cross-Chapter Box 5 in Chapter 1}
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Earlier vegetation greening under climate change raises evapotranspiration and thus lowers spring soil moisture, yet the extent and magnitude of this water deficit persistence into the following summer remain elusive. We provide observational evidence that increased foliage cover over the Northern Hemisphere, during 1982–2011, triggers an additional soil moisture deficit that is further carried over into summer. Climate model simulations independently support this and attribute the driving process to be larger increases in evapotranspiration than in precipitation. This extra soil drying is projected to amplify the frequency and intensity of summer heatwaves. Most feedbacks operate locally, except for a notable teleconnection where extra moisture transpired over Europe is transported to central Siberia. Model results illustrate that this teleconnection offsets Siberian soil moisture losses from local spring greening. Our results highlight that climate change adaptation planning must account for the extra summer water and heatwave stress inherited from warming-induced earlier greening.
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Satellite observations show that leaf area index (LAI) has increased globally since 1981, but the impact of this vegetation structural change on the global terrestrial carbon cycle has not been systematically evaluated. Through process-based diagnostic ecosystem modeling, we find that the increase in LAI alone was responsible for 12.4% of the accumulated terrestrial carbon sink (95 ± 5 Pg C) from 1981 to 2016, whereas other drivers of CO2 fertilization, nitrogen deposition, and climate change (temperature, radiation, and precipitation) contributed to 47.0%, 1.1%, and -28.6% of the sink, respectively. The legacy effects of past changes in these drivers prior to 1981 are responsible for the remaining 65.5% of the accumulated sink from 1981 to 2016. These results refine the attribution of the land sink to the various drivers and would help constrain prognostic models that often have large uncertainties in simulating changes in vegetation and their impacts on the global carbon cycle.
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Elevated CO2 (eCO2) experiments provide critical information to quantify the effects of rising CO2 on vegetation1–6. Many eCO2 experiments suggest that nutrient limitations modulate the local magnitude of the eCO2 effect on plant biomass1,3,5, but the global extent of these limitations has not been empirically quantified, complicating projections of the capacity of plants to take up CO27,8. Here, we present a data-driven global quantification of the eCO2 effect on biomass based on 138 eCO2 experiments. The strength of CO2 fertilization is primarily driven by nitrogen (N) in ~65% of global vegetation and by phosphorus (P) in ~25% of global vegetation, with N- or P-limitation modulated by mycorrhizal association. Our approach suggests that CO2 levels expected by 2100 can potentially enhance plant biomass by 12 ± 3% above current values, equivalent to 59 ± 13 PgC. The global-scale response to eCO2 we derive from experiments is similar to past changes in greenness⁹ and biomass¹⁰ with rising CO2, suggesting that CO2 will continue to stimulate plant biomass in the future despite the constraining effect of soil nutrients. Our research reconciles conflicting evidence on CO2 fertilization across scales and provides an empirical estimate of the biomass sensitivity to eCO2 that may help to constrain climate projections.
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The restoration of trees remains among the most effective strategies for climate change mitigation. We mapped the global potential tree coverage to show that 4.4 billion hectares of canopy cover could exist under the current climate. Excluding existing trees and agricultural and urban areas, we found that there is room for an extra 0.9 billion hectares of canopy cover, which could store 205 gigatonnes of carbon in areas that would naturally support woodlands and forests. This highlights global tree restoration as our most effective climate change solution to date. However, climate change will alter this potential tree coverage. We estimate that if we cannot deviate from the current trajectory, the global potential canopy cover may shrink by ~223 million hectares by 2050, with the vast majority of losses occurring in the tropics. Our results highlight the opportunity of climate change mitigation through global tree restoration but also the urgent need for action.
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Predicting the responses of streamflow to changes in forest management is fundamental to the sustainable regulation of water resources. However, studies of changes in forest cover have yielded unclear and largely unpredictable results. Here we compile a comprehensive and spatially distributed database of forest-management studies worldwide, to assess the factors that control streamflow response to forest planting and removal. We introduce a vegetation-to-bedrock model that includes seven key landscape factors in order to explain the impacts of forest removal and planting on water yield. We show that the amount of water stored in a landscape is the most important factor in predicting streamflow response to forest removal, whereas the loss of water through evaporation and transpiration is the most important factor in predicting streamflow response to forest planting. Our findings affect model parameterizations in climate change mitigation schemes (involving, for example, afforestation or deforestation) in different geologic and climate regions around the world, and inform practices for the sustainable management of water resources.
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Seasonality in photosynthetic activity is a critical component of seasonal carbon, water and energy cycles in the Earth system. This characteristic is a consequence of plant's adaptive evolutionary processes to a given set of environmental conditions. Changing climate in northern lands (>30°N) alters the state of climatic constraints on plant growth, and therefore, changes in the seasonality and carbon accumulation are anticipated. However, how photosynthetic seasonality evolved to its current state, and what role climatic constraints and their variability played in this process and ultimately in carbon cycle is still poorly understood due to its complexity. Here, we take the ‘laws of minimum’ as a basis and introduce a new framework where the timing (Day of Year) of peak photosynthetic activity (DOYPmax) acts as a proxy for plant's adaptive state to climatic constraints on its growth. Our analyses confirm that spatial variations in DOYPmax reflect spatial gradients in climatic constraints as well as seasonal maximum and total productivity. We find a widespread warming‐induced advance in DOYPmax (−1.66 ± 0.30 days decade⁻¹, P < 0.001) across northern lands, indicating a spatio‐temporal dynamism of climatic constraints to plant growth. We show that the observed changes in DOYPmax are associated with an increase in total gross primary productivity through enhanced carbon assimilation early in the growing season, which leads to an earlier phase shift in land‐atmosphere carbon fluxes and an increase in their amplitude. Such changes are expected to continue in the future based on our analysis of Earth System Model (ESM) projections. Our study provides a simplified, yet realistic framework based on first principles for the complex mechanisms by which various climatic factors constrain plant growth in northern ecosystems. This article is protected by copyright. All rights reserved.
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Empirical studies report several plausible correlations between transforms of spectral reflectance, called vegetation indexes, and parameters descriptive of vegetation leaf area, biomass and physiological functioning. However, most indexes can be generalized to show a derivative of surface reflectance with respect to wavelength. This derivative is a function of the optical properties of leaves and soil particles. In the case of optically dense vegetation, the spectral derivative, and thus the indexes, can be rigorously shown to be indicative of the abundance and activity of the absorbers in the leaves. Therefore, the widely used broad-band &near-infrared vegetation indexes are a measure of chlorophyll abundance and energy absorption.