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# Growing biomass carbon stock in China driven by expansion and conservation of woody areas

Zhanmang Liao, Binbin He, Rui Chen, Xingwen Quan, Chao Yue, Mengyang Xu, Mengyu Wang, Kaiguang Zhao, Philippe Ciais, Ramdane Alkama, et al.

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Zhanmang Liao, Binbin He, Rui Chen, Xingwen Quan, Chao Yue, et al., Growing biomass carbon stock in China driven by expansion and conservation of woody areas. Nature Geoscience, 2024, 17 (11), pp.1127-1134. 10.1038/s41561-024-01569-0. hal-04831748

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# Growing biomass carbon stock in China driven by expansion and conservation of woody areas

Received: 26 February 2024

Accepted: 19 September 2024

Published online: 30 October 2024

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Zhanmang Liao Ⓔ¹,⁸, Chao Yue Ⓔ²,³,⁸✉, Binbin He Ⓔ¹✉, Kaiguang Zhao⁴, Philippe Ciais Ⓔ⁵, Ramdane Alkama⁶, Giacomo Grassi⁶, Stephen Sitch Ⓔ⁷, Rui Chen Ⓔ¹, Xingwen Quan Ⓔ¹, Mengyang Xu Ⓔ³ & Mengyu Wang³

The Paris Agreement mandates that signatory countries enhance the transparency of their national greenhouse gas inventories. China's inventories have reported substantial forest carbon gains using ground-based forest plot measurements, but independent satellite-based support for such inventories is lacking and the contributions from human management and anthropogenic environmental changes (atmospheric CO₂ growth, climate change and nitrogen deposition) are unknown. Here we use remote sensing and vegetation modelling to investigate the changes in woody biomass carbon and their drivers across China from 2001 to 2020. Our results show a forest cover increase of 6.2% (59.2 Mha) over this period and a woody biomass carbon sink of 208.6 ± 51.8 TgC yr⁻¹, consistent with the national inventories. The conservation of forest and woodland areas made an unexpectedly large contribution (59.2%) to the observed sink, with an additional 29.4% from anthropogenic expansion. Of these management-driven sinks, 53.7% (99.2 TgC yr⁻¹) is attributed to a direct management effect and the remaining 46.3% to the effects of environmental changes. China's ecological restoration projects contributed 73.5% of the direct management effect. Our study provides satellite-based evidence to support China's inventories and underscores the crucial role of human management in the nation's woody carbon balance.

Sufficient transparency in the national greenhouse gas inventories (NGHGs) of signatory nations is crucial for the success of the Paris Agreement¹. Globally, forests are the most important component of the land carbon balance² and are expected to account for a quarter of the emissions reductions by 2030³. However, many countries—including China—establish NGHGs for the forest sector using almost entirely ground-based national forest inventories (NFIs)⁴⁻⁷, which have low transparency and large uncertainties.

A key aspect of transparency/uncertainty in NGHGs relates to the
contributions of anthropogenic land management to the land carbon

balance⁸⁻¹⁰. Quantifying such contributions is highly relevant for China, given the nation's extensive ecological restoration efforts¹¹,¹². Previous studies have addressed this issue by using either vegetation modelling¹³ or combining ground surveys with government statistics¹¹, but they are not validated by satellite observations. For example, China's recent growth in forest area documented by NFIs was found to be inconsistent with any of the published land cover datasets (Extended Data Fig. 1). Moreover, previous studies have focused on the contribution of anthropogenic forest expansion¹³, whereas the contribution made by the active conservation of forest and woodland has been largely ignored.
The uncertain role of human management is exacerbated by the confounding effects of the environmental changes that co-occur with land management. Anthropogenic environmental changes, including increasing atmospheric carbon dioxide (CO₂) concentration, climate change and nitrogen deposition, tend to collectively stimulate plant growth¹⁴⁻¹⁶, explaining the faster growth documented for recently established forests compared with that of forests established a century ago¹⁷,¹⁸. In assessments of the global carbon budget¹⁵, the additional forest growth stimulated by environmental changes is referred to as 'the environmental effect', whereas the carbon accumulation rate that is driven solely by forest establishment and subsequent growth is referred to as 'the direct management effect'. Differentiating these two effects is important, because the latter is considered truly 'anthropogenic', whereas the former is the system response of the global carbon cycle to anthropogenic forcings¹⁹. For China, previously reported forest biomass carbon sinks based on NFIs²⁰,²¹ or remote sensing²²,²³ do not separate the environmental and direct management effects. Modelling efforts have been made to separate them, but these are often based on a single model and fail to account for forest rotation (for example, see Yu et al.¹³).

This study uses satellite observations to validate China's NGHGs and to quantify the environmental and direct management contributions to China's woody carbon balance. We developed satellite-based estimates of woody area dynamics and woody biomass carbon changes for 2001–2020 across China using both optical remote sensing and spaceborne LiDAR (light detection and ranging) observations²⁴. Simulations from 17 dynamic global vegetation models (DGVMs)¹⁵ are used to estimate the environment effect, with the difference from the observed carbon sink being quantified as the direct management effect, followed by further attribution to different ecological restoration projects (see Methods and Extended Data Fig. 2).

## Overview of woody biomass carbon sink and its drivers

To aid navigation through the following sections, we first present an overview of the key results (Fig. 1). A total (including both above- and below-ground) woody biomass carbon sink of 208.6 ± 51.8 TgC yr⁻¹ (mean ± standard error) across China for 2001–2020 was derived (Fig. 1a). This sink was separated into contributions from eight different land management types (Fig. 1b). For each land management type, the separate contributions of environmental changes and direct human management were determined (Fig. 1c). Finally, part of the direct-management-induced carbon sink was attributed to several national ecological restoration projects (Fig. 1d).

## Forest expansion and woody biomass carbon sink

We combined the 30-m-resolution tree cover from Hansen et al.²⁵ with MODIS (MODerate-resolution Imaging Spectroradiometer) images to generate annual tree-cover maps at 500 m resolution for 2001–2020. Woody areas are defined as those with tree cover of ≥8% and were further separated into forest (tree cover ≥ 31.2%) and woodland (8% ≤ tree cover < 31.2%) (see Methods). Our results showed that national forest cover grew steadily from 18.1% in 2001 to 24.3% in 2020—an annual growth rate of 0.33% per year (Fig. 2a). According to the NFIs²⁶, China's forest cover increased from 18.2% in the sixth inventory (a five-year time window centred on 2001) to 23.0% in the ninth inventory (centred on 2016)—an annual growth rate of 0.32% per year. The agreement in forest cover change between our estimate and the NFIs provides remote-sensing evidence to support the national forest expansion reported by the NFIs (Extended Data Fig. 1). Across the provinces, changes in satellite-based forest cover during four individual five-year inventory periods covering 1998–2018 also match well with those reported by the NFIs (Extended Data Fig. 3).

The GEDI (Global Ecosystem Dynamics Investigation) above-ground biomass product, derived from spaceborne LiDAR²⁴, was

combined with MODIS images and a forest biomass root-to-shoot ratio map to derive annual maps of biomass carbon stock at 500 m resolution for 2001–2020 (see Methods). The woody total live biomass carbon stock in China, including both forest and woodland, showed a continuous increase between 2001 and 2020, yielding a carbon sink (ΔC<sub>biomass</sub>) of 208.6 ± 51.8 TgC yr⁻¹ (Fig. 2b). This estimate matches well with the forest carbon sink of 208.8 TgC yr⁻¹ for 2005–2014 reported by China's NGHGs<sup>7,27,28</sup> (light green bar in Fig. 2c). The carbon sinks for above- and below-ground biomass were 179.2 ± 40.7 and 29.4 ± 6.9 TgC yr⁻¹, respectively, according to this study. Combining our estimated ΔC<sub>biomass</sub> with the carbon sink in soil organic carbon and surface litter in all ecosystems reported by Fang et al.<sup>29</sup> yields a total terrestrial carbon sink of 290.2 ± 51.8 TgC yr⁻¹ (dark green bar in Fig. 2c). This number is close to China's terrestrial carbon sink of 252.5 TgC yr⁻¹ for 2005–2014 as reported by the NGHGs<sup>7,27,28</sup> (light green bar in Fig. 2c), and falls within the range of 170–350 TgC yr⁻¹ (Fig. 2c) of previous estimates<sup>30</sup> using various methods.

Spatially, China's woody biomass carbon sink during 2001–2020 is concentrated in northeastern China (Heilongjiang and Neimenggu), central-southern China (Shaanxi), southwestern China (Sichuan, Chongqing and Yunnan) and southern China (Guangxi, Guizhou and Fujian) (Fig. 2d,e). The reported spatial pattern of ΔC<sub>biomass</sub> is confirmed by a strong correlation at the province scale between our estimated forest above-ground biomass carbon sink (ΔC<sub>AGB</sub>) and the NFI-based changes in forest growing stock volume (ΔGSV) (sixth to ninth inventories) for 2001–2016 (Fig. 2e).

## Contributions from different land management types

The woody areas in China were classified, respectively, into eight types of land management that are ranked in decreasing order of management intensity (Fig. 3a and Extended Data Fig. 4; Methods): types 1 and 2, rotational forest and rotational woodland; types 3 and 4, afforestation and deforestation; types 5 and 6, woodland expansion and woodland clearing; and types 7 and 8, stable forest and stable woodland. 'Afforestation' and 'deforestation' (or 'woodland expansion' and 'woodland clearing'), respectively, refer to a one-time conversion to or from forest (woodland); 'rotational forest' and 'rotational woodland' involve multiple conversions between forest (woodland) and non-forest (non-woody), probably resulting from cycles of wood harvest and regeneration; and 'stable forest' and 'stable woodland' indicate the persistence of forest or woodland, which may, however, be subject to passive management such as conservation or protection. The areas and carbon balances for all of these management types are detailed in Extended Data Table 1.

Of China's 330.8 Mha of woody area, 48.0% (158.8 Mha) is under active management (that is, belongs to land management types 1–6) with the other half (52.0%) under potential passive management (types 7 and 8) (Fig. 3b). The satellite-derived areas of afforestation and woodland expansion for different provinces correlate moderately well with the anthropogenic afforestation established through ecological restoration projects reported in the Chinese Forestry Statistical Yearbooks (CFSYS)²⁶ for 1996–2018 (R² = 0.59, P < 0.05; Extended Data Fig. 5). The observed extensive afforestation in the southwestern provinces of Sichuan, Chongqing, Yunnan and Guizhou (Fig. 3a) stems largely from the Natural Forest Conservation Program (NFCP) and the Grain for Green Program (GGP) (Extended Data Fig. 6). In the northern and central provinces of Hebei, Shanxi, Shaanxi and Gansu, the observed areas of afforestation and woodland expansion (Fig. 3a) coincide with known anthropogenic afforestation from the ecological restoration projects of GGP, the Shelter Forest Projects (SFPs) and the Beijing–Tianjin Sand Source Control Project (SCP) (Extended Data Fig. 6).

The rotational forest and rotational woodland management types were observed mostly in southwestern China (Yunnan and Sichuan), southern China (Guangxi, Guangdong and Fujian) and northeastern
<figure><img src="image_4.png" /><figcaption>**Fig. 1 |** Results overview: satellite-derived woody biomass carbon sink in China for 2001–2020 and its separation into different contributions. **a,** Satellite-derived woody biomass carbon sink. **b,** Separating the observed carbon sink by drivers of different land management types. **c,** Environmental and direct management contributions for each land management type. **d,** Direct management carbon-sink effects for several national ecological restoration projects: NFCP, Natural Forest Conservation Program; GGP, Grain for Green Program; SFPs, the Shelter Forest Projects, which include the Three-North</figcaption></figure>

Shelter Forest Program, the Yangtze River Shelter Forest Program, the Zhujiang River Shelter Forest Program, the Coastal Shelter Forest Program and the Taihang Mountain Greening Program; SCP, the Beijing–Tianjin Sand Source Control Project; FHTP, the Fast-growing and High-yielding Timber Program. ‘Other projects’ comprises the conservation and expansion of forest and woodland via ecological restoration that does not belong to any of the above-mentioned projects. The data are also presented in tabular form in Extended Data Tables 1 and 2.

China (Heilongjiang and Neimenggu) (Fig. 3a). The satellite-derived rotational forest area was found to correlate moderately well with the government-reported volume of timber and fuelwood harvest for 2003–2018 across different provinces ($R^2$ = 0.38–0.57, *P* < 0.01; Supplementary Fig. 1).

The largest contribution to the observed woody carbon sink comes from the stable forest (117.6 TgC yr⁻¹) and stable woodland (6.0 TgC yr⁻¹) management types, with their total sink being twice as much as those from afforestation (49.8 TgC yr⁻¹) and woodland expansion (11.5 TgC yr⁻¹) (Fig. 3c and Extended Data Table 1). Rotational forest and rotational woodland activities contributed a small carbon sink of 25.1 TgC yr⁻¹, whereas deforestation and woodland clearing types of management contributed a negligible carbon source of ~1.3 TgC yr⁻¹.

On a per-area basis, the management types associated with forest areas have greater carbon intensity than those associated with woodland (Fig. 3d). Stable forest (0.76 MgC yr⁻¹ ha⁻¹) and stable woodland (0.33 MgC yr⁻¹ ha⁻¹) activities have higher carbon-sink intensities than rotational forest (0.55 MgC yr⁻¹ ha⁻¹) and rotational woodland (0.19 MgC yr⁻¹ ha⁻¹) management types, which seems plausible because the former types are subjected to both environmental changes and conservation, whereas for the latter types the harvest-induced carbon source may partly cancel out the carbon gain from post-harvest regrowth.

## Environmental versus direct management effects

Our DGVM-based analysis and attribution of China’s woody biomass carbon sink shows that for 2001–2020, the carbon sink driven by environmental changes reached 109.5 TgC yr⁻¹ (Fig. 4a), whereas the direct management contribution was 99.1 TgC yr⁻¹ (Fig. 4b). The spatial distribution of the environmental effect is relatively uniform. Carbon sinks induced via direct management, by contrast, are concentrated in northeastern, central and southwestern regions of China, corresponding to areas of afforestation and woodland expansion (Figs. 4b and 3a). The extensive management-induced carbon sink in eastern China is dominated by forest conservation (that is, stable forest management).

More than half of the biomass carbon sink from the afforestation and woodland expansion activities (59.1% or 36.2 TgC yr⁻¹) was contributed by a direct management effect (Fig. 4c and Extended Data Table 1). By comparison, for stable forest and stable woodland, direct management has a lower contribution (51.0%, 63.0 TgC yr⁻¹), consistent with their lower management intensity than afforestation and woodland expansion. The direct management effect in the stable forest and stable woodland activities comes about because the forest and woodland conservation measures, such as the mountain closure implemented by various ecological restoration projects, limit accessibility for harvesting and grazing, hence allowing recovery of the carbon stock from past human exploitation. Collectively, combining the four land management types (that is, conservation and expansion of forest and woodland), the direct management effect contributed a carbon sink of 99.2 TgC yr⁻¹ (53.7%) out of an observed carbon sink of 184.8 TgC yr⁻¹.

By contrast, when combining the rotational forest and rotational woodland activities, direct management exerts a negligible contribution of 10.4% (2.6 TgC yr⁻¹) to their observed carbon sink. Although rotational forest and rotational woodland have the highest management intensity, this negligible contribution via direct management suggests that the carbon-sink effect of forest (or woodland) establishment has been largely cancelled out by the carbon-source effect of wood harvest. Finally, the direct-management-induced carbon source for deforestation and woodland clearing activities, which exceeds the environmental carbon sink, signifies a strong management effect–similar, but in the opposite sense, to that of the afforestation and woodland expansion activities.

## Contributions from ecological restoration projects

The contributions made by different ecological restoration projects to the carbon sinks driven by direct management were quantified by combining the data that we derived for the direct-management-induced carbon sink with those reported in the CFSYs for woody areas subject
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<figure><img src="image_8.png" /><figcaption>**Fig. 2** | Satellite-derived forest expansion and woody biomass carbon sink in China for 2001–2020. **a,** Changes in the land area percentages of woody (forest and woodland) and non-woody areas. The grey circles represent the forest cover reported by the NFIs (from left to right, sixth to ninth inventories). **b,** Annual woody biomass carbon stocks. The blue solid line represents the mean value of 1,000 Monte Carlo samples (see Methods) and the shaded area denotes the standard error. **c,** Comparison of satellite-derived biomass carbon sink (dark green) with terrestrial carbon balances derived using other, independent approaches: forest inventory (blue), vegetation modelling (pink) and atmospheric inversion (orange). The light green bar shows the carbon-sink data for 2005–2014 from forest and other ecosystems reported by the NGHGIs.</figcaption></figure>

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The dark green bar indicates the woody biomass carbon sink in this study (with the mean value of 1,000 Monte Carlo samples (indicated by light grey dots) shown in yellow and the error bars denoting the standard error; see Methods) plus the terrestrial litter and soil organic carbon (SOC) sinks derived from Fang and colleagues²⁹. Data from refs. 21,29,36–40. **d,** Spatial distribution of satellite-derived woody biomass carbon sink. **e,** Relationship between satellite-derived forest above-ground biomass carbon sink (ΔC<sub>AGB</sub>) and inventory-based change in growing stock volume (ΔGSV) for different provinces. The blue solid line indicates a linear regression and the shaded area denotes the 95% prediction intervals. **R**, correlation coefficient.

to ecological restoration projects²⁶. Two major ecological restoration activities were identified: that is, anthropogenic afforestation, which corresponds to the land management types of afforestation and woodland expansion, and the conservation of forest and woodland (including mountain closure), which corresponds to the stable forest and stable woodland land management types.

The cumulative area of anthropogenic afforestation recorded by the CFSYs reached 114.2 Mha for 1996–2018, close to our derived total area of 88.0 Mha for afforestation and woodland expansion activities in 2001–2020. Hence, we assume that the direct-management-induced carbon sink from afforestation and woodland expansion can be completely ascribed to anthropogenic afforestation.

According to the CFSYs, the GGP made the largest contribution (28.6 Mha, 25.0%) to China's anthropogenic afforestation area, followed

by the cluster of SFPs (18.6 Mha or 16.3%) and the NFCP (11.9 Mha or 10.4%). Small contributions came from the SCP (7.3 Mha or 6.4%) and the Fast-growing and High-yielding Timber Program (FHTP; 0.33 Mha or 0.3%) (Fig. 5a). Collectively, these projects accounted for 58.4% of the total anthropogenic afforestation area, with the remainder being ascribed to other projects. A proportional allocation of the management-induced carbon sink for different projects and for different provinces, by their respective area percentages, shows that the five key ecological restoration projects (that is, the GGP, SFPs, NFCP, SCP and FHTP), not including other projects, generated a carbon sink of 19.3 TgC yr⁻¹ (Fig. 5a and Extended Data Table 2).

The total conservation area of forest and woodland for the various projects, as reported in the CFSYs, was 97.2 Mha and accounted for about half the total area of stable forest and stable woodland
<figure><img src="image_10.png" /><figcaption>**Fig. 3** | Satellite-derived woody biomass carbon sink driven by different land management activities during 2001–2020. **a–d**, Spatial distribution of different land management types in China between 2001 and 2020 (a), their percentage area values in terms of the total land area of China (b), their biomass carbon balance values (c) and their biomass carbon balances on a per-unit-area</figcaption></figure>

basis (d). In c and d, the bar plots represent the mean values of 1,000 Monte Carlo samples (indicated individually by light grey dots) (see Methods) and the error bars denote the standard error. The data for b–d are also provided in tabular form in Extended Data Table 1.

derived via remote sensing. The remainder is attributed to 'natural recovery' (Fig. 1d), which includes the recovery of forest and woodland unplanned by the government, due, for example, to a shift of the population from rural to urban areas²³. A proportional allocation of the direct-management-induced carbon sink to such natural recovery and the ecological restoration projects, and, further, according to the conservation areas of different projects in different provinces, shows that the NFCP contributed the biggest carbon sink (31.4 TgC yr⁻¹) of all the projects (Fig. 5b), whereas 26.2 TgC yr⁻¹ was ascribed to natural recovery (Fig. 1d).

When considering both anthropogenic afforestation and the conservation of woody areas, the NFCP provides the biggest direct-management-induced carbon sink of 35.7 TgC yr⁻¹, followed by the GGP (9.6 TgC yr⁻¹) and SFPs (4.6 TgC yr⁻¹), with minor contributions

from the SCP (1.8 TgC yr⁻¹) and the FHTP (0.1 TgC yr⁻¹) (Extended Data Table 2). The afforestation and conservation activities not recorded in the five key ecological restoration projects (that is, other projects) provided a contribution of 21.2 TgC yr⁻¹. Collectively, the ecological restoration projects contributed 73.0 TgC yr⁻¹, explaining 73.7% of the direct-management-induced carbon sink (99.1 TgC yr⁻¹) from the conservation and expansion of forest and woodland areas.

## Uncertainties and policy implications

Our estimated woody biomass carbon sink in China is comparable to other recent satellite-based estimates. Chang et al.³¹ and Zhang et al.²³ reported the above-ground biomass carbon sink of Chinese forests to be, respectively, 170 TgC yr⁻¹ (for 2013–2019) and 280 ± 50 TgC yr⁻¹ (for 2002–2019). Our estimate for 2001–2020 (179.2 ± 40.7 TgC yr⁻¹) falls
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<figure><img src="image_13.png" /><figcaption>**Fig. 4** | Environmental and direct management effects of the woody biomass carbon sink in China for 2001–2020. **a,b,** Spatial distribution of the woody biomass carbon sink driven by the environmental effect (**a**) and the direct management effect (**b**). **c,** Separation of the satellite-derived carbon sink for different land management types into environmental and direct management effects. These data are also provided in Extended Data Table 1.</figcaption></figure>

between them. However, few remote-sensing studies have examined the role of land management in China's woody carbon sink. An exception to this is Tong et al.²², who focused on southern China only and reported that anthropogenic forest expansion dominated extensive carbon sequestration. Although their finding is also evident in Fig. 3a, we also confirmed a large contribution from the conservation of forest and woodland by extending the research scope to the entire country.

The overall credibility of DGVMs for deriving the environmental carbon effect has been established through the fact that the simulated environmental carbon sink at a global scale can successfully close the global carbon budget¹⁵. Alternatively, the separation between

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 5** | Carbon-sink contributions from ecological restoration projects. **a,b,** Direct-management-induced carbon sinks attributed to anthropogenic afforestation (**a**) and conservation of forest and woodland (**b**) in China's different ecological restoration projects. The inner rings show the percentage areas of the different projects; the outer rings show the carbon-sink values contributed by different projects. All data are provided in tabular format in Extended Data Table 2.</figcaption></figure>

the direct management effect and the environmental effect may be inferred by comparing the carbon sinks between paired sites with and without ecological restoration¹¹. From a national ground survey that uses such paired sites, Lu et al.¹¹ estimated, on a per-area basis, a forest carbon sink of 0.30 MgC yr⁻¹ ha⁻¹ for the environmental effect over China's ecological restoration area, which is very close to the environmental effect of 0.32 MgC yr⁻¹ ha⁻¹ derived here. For the four key ecological restoration projects of China (the NFCP, GGP, SFPs and SCP), Lu and co-workers¹¹ reported a direct-management-induced sink of 45.3 TgC yr⁻¹, which also compares favourably with our estimate of 51.7 TgC yr⁻¹ (Extended Data Table 2).

However, it is worth noting that the direct management effect quantified in this study is consistent with the definition used in the Global Carbon Project in that it excludes the effect of environmental changes¹⁰ but differs from the definition of 'management effect' in the NGHGs, in which a 'managed land proxy' is used and any observed carbon sinks/sources over designated 'managed land' are attributed entirely to the management effect³². This difference in terminology has been highlighted in the, most recent, Sixth Assessment Report of the Intergovernmental Panel on Climate Change³³ and remains
an issue to be addressed. For China's NGHGs, all land in China is simply designated as 'managed land'; therefore, the 'management effect' in China's NGHGs and the effect derived in this study are not comparable.

Under the Enhanced Transparency Framework of the Paris Agree-
ment, all nations will biannually submit their NGHGs to examine,
every five years, how the international community is collectively mov-
ing towards the objective of the Paris Agreement³⁴. In this context,
independent verification of NGHGs, especially land carbon fluxes
ascribed to human management effects (with a possible reconciliation
of the terminology differences, as described above), will be crucial for
increasing confidence in the assessment of climate progress. The use
of remote sensing to quantify the carbon fluxes due to human land
management has been especially advocated³⁵. Our study, by providing
remote-sensing evidence for a management-induced carbon sink in
China, represents an important step in this direction.

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© The Author(s), under exclusive licence to Springer Nature Limited 2024

<footnote n="1">¹School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China. ²Institute of Soil and Water Conservation, Northwest Agriculture and Forestry University, Yangling, China. ³College of Natural Resources and Environment, Northwest Agriculture and Forestry University, Yangling, China. ⁴Ohio Agricultural and Research Development Center, School of Environment and Natural Resources, The Ohio State University, Wooster, OH, USA. ⁵Laboratoire des Sciences du Climat et de l'Environnement, CEA/CNRS/UVSQ/Université Paris Saclay, Gif-sur-Yvette, France. ⁶Joint Research Centre, European Commission, Ispra, Italy. ⁷Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK. ⁸These authors contributed equally: Zhanmang Liao, Chao Yue.✉e-mail: chaoyuejoy@gmail.com; binbinhe@uestc.edu.cn</footnote>
# Methods

## Methodology overview

In this study, we use remote sensing to quantify carbon sequestration in woody biomass in China for 2001–2020 and examine how it is driven by different land management activities. These estimates of carbon sequestration are then combined with data from multiple DGVMs to separate out the contributions of environmental changes and direct human land management. Finally, the carbon-sequestration effects of direct human management are apportioned to different ecological restoration projects in China. An overview of the workflow is provided in Extended Data Fig. 2.

Annual changes in woody area (for both forest and woodland) and the associated live biomass carbon stock (both above and below ground) between 2001 and 2020 were derived at a resolution of 500 m on the basis of MODIS data for China (Extended Data Fig. 2a–d). For this purpose, daily MODIS Nadir BRDF-adjusted reflectance (NBAR) data were first gap-filled and noise-filtered to derive annual composited MODIS images, which were further used to derive annual tree-cover distributions (Extended Data Fig. 2a,b). Change-point detection and piecewise fitting were used to filter out the temporal noise in the annual time series data, yielding piecewise-fitted (PWF) annual time series of MODIS images and tree cover (Extended Data Fig. 2c). These data were then used to estimate woody live biomass (Extended Data Fig. 2d). A total of eight land management types for woody vegetation were identified, on the basis of temporal changes in tree-cover time series (Extended Data Fig. 2e), with the carbon sequestration induced by each land management type being quantified. Carbon-sequestration effects due to environmental changes (including climate change, atmospheric CO₂ growth and nitrogen deposition) were simulated using 17 different DGVMs. The direct management effects were then quantified as the difference between the observed carbon-sequestration rate and that induced by the environmental changes (Extended Data Fig. 2f). Finally, the carbon-sink contributions of six different ecological restoration projects were quantified by partitioning the observed direct management effects over the land management types of woody expansion and woody conservation into different projects as recorded in the CFSYS²⁶ (Extended Data Fig. 2g).

## MODIS NBAR data pre-processing for gap-filling, spike-removal and annual time series construction

The MODIS MCD43A4 NBAR product was used as the primary input data for mapping of the tree cover and above-ground biomass (AGB). This product provides the composited daily bidirectional reflectance distribution function (BRDF), which is derived from the original daily BRDF observations for a 16-day period centred around a given day⁴¹. To reduce data storage and computation loads, while maintaining sufficient observations, only the NBAR values for the first day of each eight-day interval throughout a given year were used, yielding 46 images per year. Invalid NBAR values were identified and filtered out on the basis of the data-quality band provided by the MCD43A4 spectroradiometer. In addition, remaining NBAR observations with an NDVI (normalized difference vegetation index) that is lower than or equal to zero, or with an NDSI (normalized difference snow index) larger than or equal to zero, were also filtered out because these pixels were probably obscured or covered by cloud, water or snow⁴².

Data gaps due to the removal of invalid observations were first gap-filled using the mean of observations for the same day in the preceding and following years or, if one of these two observations was also missing, using a single value. A linear interpolation over time was then used to fill the remaining gaps. Because of the frequent data gaps due to snow cover for northern China in winter and spring, we only used observations from 1 May to 2 November, roughly covering the growing season across the whole country, which resulted in 24 eight-day observations for each year. These 24 images were used to derive two composited MODIS images for each year, the annual mean (AM) and

the annual standard deviation (ASD), for six NBAR bands (bands 1–7 excluding band 3, the blue band) and two NBAR-based vegetation indices (that is, the two-band enhanced vegetation index (EVI2) and the normalized difference infrared index (NDII)). We applied a median filter with a three-year window on the annual AM and ASD time series to remove potential spikes. Hence the first- and last-year values of the original time series were removed, reducing the period of data availability from 2000–2021 to 2001–2020.

## Spatial mapping of tree cover and separating woody areas into forest and woodland

The random forest (RF) model was used to generate the tree-cover distribution at a spatial resolution of 500 m. The Hansen tree-cover product²⁵ for 2000 was used as reference data after upscaling from 30 m to 500 m. The input predictors consist of the NBAR AM images of the six MODIS bands, EVI2 and NDII (described above) for 2001 and the elevation data derived from the Shuttle Radar Topography Mission digital elevation model, which was aggregated from 90 m to 500 m.

To minimize the influence of the one-year difference in data acquisition time between the reference Hansen tree-cover data and the MODIS-based predictors, the 500 m tree-cover pixels with forest loss occurring during 2000–2001 (diagnosed from the Hansen forest-loss product²⁵) were excluded from the training samples. Then, 150,000 pixels were randomly sampled from the group of pixels with a tree cover larger than zero and 50,000 pixels randomly sampled from the group with tree cover equal to zero, giving a total of 200,000 samples at 500 m resolution for training of the RF model. The ‘treebagger’ package in MATLAB⁴³ was used to perform the RF modelling. Fourfold cross-validation was used to evaluate the RF model performance (Supplementary Fig. 2), with an *R*² value of 0.90 and a root mean square error (RMSE) of 9.1%. The RF model finally used to predict the tree cover for the full time series of 2001–2020 was trained using all 200,000 training samples.

Our annual tree-cover maps were then used to divide the land in China into woody and non-woody areas, with the woody areas subsequently being further separated into forest and woodland. The separation used the PWF tree cover (detailed below) instead of the raw tree cover obtained via the RF model, because the former has less of the temporal noise which can cause classification fluctuations. These fluctuations are believed to be an artefact of climate variations in the MODIS NBAR data rather than actual changes in land cover type. Details on deriving the PWF tree cover are introduced in ‘Woody live biomass carbon density mapping for 2001–2020’ in the Methods.

Forest area was defined by selecting a tree-cover threshold above which the national forest cover–derived using MODIS PWF tree-cover data–matches that derived from the China NFIs for 1999–2018. This period was selected because it covers the sixth–ninth NFIs, which correspond to four five-year periods from 1999–2003 to 2014–2018, with each NFI covering five years. The tree-cover threshold was determined as 31.2%, yielding a temporally averaged national forest cover of 21.5% for 2001–2020. Note that this static threshold was applied for the entire time series of PWF tree cover for 2001–2020 and for every 500 m MODIS pixel, and therefore spatial and temporal changes in the national forest area derived via remote sensing could still be examined and compared with the NFIs. We regarded the sixth–ninth NFIs as representative of the central year of their respective five-year windows.

A tree-cover range of 8.0–31.2% was used to define woodland area. As woodland consists mainly of sparse-canopy forest or shrubland, the lower tree-cover threshold of 8.0% was empirically selected to make the MODIS-derived woodland area approximately match the shrubland area reported by a recent nationwide systematic survey²⁹.

The 500 m pixels with a PWF tree cover of less than 8.0% (including grassland, cropland and bare land) were then classified as non-woody. As we focus on live biomass carbon stock changes in woody ecosystems in China, non-woody areas were excluded from the subsequent analysis.
## Woody live biomass carbon density mapping for 2001–2020

The annual time series of AM and ASD images derived from the MODIS NBAR data are still subject to the influence of residual noise (for example, remaining unscreened cloud or cloud shadow, or errors from gap-filling), which present as abrupt variations in the AM and ASD time series. This problem can probably never be completely solved in optical remote sensing, but piecewise fitting algorithms can alleviate its impacts, for example, the LandTrendr and BEAST algorithms<sup>44,45</sup>. They assume that any temporal changes in the annual time series, if free of the artificial noise, should be moderately continuous and smooth, unless there is a shift in the underlying vegetation conditions. Two steps are needed: the first is to detect the change points that separate the whole time series into multiple segments; the second is to fit connected piecewise linear regressions for each individual segment.

For the first step, we utilized the BEAST algorithm to detect change points in the annual time series of the RF model-generated tree cover<sup>45</sup>. To further improve the confidence of the change-point detection, another set of change points was obtained by applying BEAST to the annual time series of the normalized burn ratio which is sensitive to disturbance events<sup>44,46</sup>. Change points detected in both the normalized burn ratio and the tree-cover time series were considered to be highly robust and were ultimately used.

For the second step, on the basis of the detected change points, connected piecewise linear regressions with time were conducted for all segments of the time series and for all parameters, including the AM and ASD images and tree cover. The piecewise-fitted AM and ASD images, along with the piecewise-fitted tree cover (denoted PWF-AM, PWF-ASD and PWF-tree cover, respectively), were considered to be robust to noise and hence were used for AGB estimation.

Annual maps of AGB at a resolution of 500 m for 2001–2020 in China were derived using an RF model. The input features of the RF model included the PWF-AM and PWF-ASD images of the six MODIS NBAR bands (bands 1–7, excluding band 3) and the EVI2 and NDII vegetation indices, the PWF-tree cover and the elevation. These input predictors were selected partly following Baccini and colleagues<sup>47,48</sup>. The RF model was trained using the 1 km GEDI L4B gridded AGB product as the training samples. This product provides the mean AGB and its standard error on the basis of all footprint AGB observations (with a diameter of about 25 m) within the 1 km grid cell of the GEDI L4A product acquired between 18 April 2019 and 4 August 2021<sup>49</sup>, a period that is considered to be representative of 2020. We kept only the 1 km pixels with at least five high-quality L4A samples within the 1 km grid cell (about 5 million). Please refer to Supplementary Fig. 3 and Supplementary Method 1 for a detailed description of the generation of the GEDI L4A and L4B products and their use in our AGB mapping.

To train the RF model, values for all input features were obtained for 2020 and were resampled from 500 m to 1 km to match the spatial resolution of the GEDI L4B AGB. To evaluate the model performance, we randomly selected 10% of the full samples (about 5 million 1 km pixels in China) for training and the remaining 90% for cross-validation. The treebagger package in MATLAB was used to perform the RF modelling. Results showed an *R*² value of 0.54 and an RMSE of 48.5 Mg ha⁻¹ for AGB estimation (Supplementary Fig. 4). The full sample, with about 5 million pixels, was used to train the RF model finally used for AGB mapping at 500 m resolution for 2001–2020. The AGB estimates were further compared with forest inventory data and previously published AGB maps (Supplementary Method 2 and Supplementary Figs. 5 and 6). The uncertainty (standard error) of the estimated AGB was quantified by integrating the errors from three different sources: the error in the GEDI reference AGB, which was directly provided by the L4B product; the error in the RF model prediction, which was simulated using a nonlinear model with a homogeneous error; and the error from spatial autocorrelation, which was quantified using a powered exponential covariance function<sup>50</sup>.

The woody below-ground biomass (BGB) for 2001–2020 was derived by applying a gridded woody root-to-shoot ratio (R:S) map to the

estimated annual AGB maps. The R:S map at 1 km resolution was derived as the ratio between the 1 km map of forest root biomass for 2010 published by Huang et al.<sup>51</sup> and our estimated AGB (resampled from 500 m to 1 km) for the same year. The static R:S map was then resampled from 1 km to 500 m for BGB estimation. As the R:S map did not cover all of the woody pixels, the mean R:S value for all woody pixels within a given province was assigned to the pixels whose R:S value was missing. The total woody live biomass was calculated as the sum of AGB and BGB. To convert biomass to carbon, a constant carbon concentration of 0.47 was used<sup>52</sup>.

The uncertainty (standard error) of the estimated BGB was derived as the product of the AGB uncertainty and the R:S ratio. The standard error for total biomass was calculated as the sum of the standard errors for AGB and BGB. On the basis of the estimated biomass and its uncertainty (standard error), we used the Monte Carlo method to sample 1,000 times, assuming a normal distribution of biomass, with each sample resulting in a 20 year time series of biomass maps of China. The annual time series of woody biomass carbon stock in China (Fig. 2b), the carbon sink between 2001 and 2020 (calculated as the difference in biomass between 2020 and 2001 further divided by 19; Fig. 3) and their associated uncertainties (standard errors) were all calculated on the basis of the 1,000-sample annual biomass maps.

## Characterizing woody vegetation dynamics by separating into different land management types

The woody vegetation dynamics in China were divided into a total of eight land management types. The classification was inspired partly by the forest management classification of Tong et al.<sup>22</sup> and is based on the PWF tree-cover dynamics (Supplementary Table 1 and Extended Data Fig. 4).

The classifications are as follows. (1) Afforestation, where the PWF tree-cover time series indicates a state shift from non-forest (that is, woodland or non-woody) to forest and remains as forest afterwards. Afforestation may include 'anthropogenic afforestation', which is caused by intentional human activities such as tree planting and natural stand regeneration following natural disturbance. For China, however, the occurrence of natural disturbances is very limited. According to Tong et al.<sup>22</sup> and Wu et al.<sup>53</sup>, only 7% of satellite-derived forest loss was collocated with natural disturbances (mainly fire) in China in the past two decades. (2) Woodland expansion, with the land cover shifting from non-woody to woodland and remaining as woodland afterwards. Similarly to afforestation, woodland expansion may also include anthropogenic afforestation and natural regeneration. Anthropogenic afforestation is potentially responsible for woodland expansion because many anthropogenic afforestation projects are located in the arid and semi-arid areas of northern China, resulting in landscapes with a sparse tree canopy<sup>22,54</sup>. (3) Stable forest, where the PWF tree-cover values of the entire time series were greater than 31.2% throughout 2001–2020. (4) Stable woodland, for which the PWF tree-cover values of the entire time series were constantly lower than 31.2% but greater than 8.0%. (5) Rotational forest, where the time series of tree cover shows multiple conversions between forest and non-forest (or vice versa). (6) Rotational woodland, where the time series of tree cover was always lower than 31.2% but showed multiple conversions between woodland and non-woody area (or vice versa). (7) Woodland clearing, where the pixel had a woodland state in 2001 but then changed to non-woody and never subsequently recovered to woodland. (8) Deforestation, where the pixel had a forest state in 2001 but then changed to non-forest and never recovered to forest afterwards. Note that deforestation includes the conversion of both forest to woodland (that is, forest degradation) and forest to non-woody area (complete deforestation).

## Separating environmental and direct management effects on woody carbon balance

The observed carbon sinks in woody biomass in China are driven by two effects: the effect of transient, largely anthropogenic environmental
changes on land ecosystems (the environmental effect), including increasing atmospheric CO₂ concentration, climate change and nitrogen deposition, and the effect of direct human land use and land management by assuming no environmental changes¹⁵,¹⁹. Collectively, the different aspects of environmental change tend to enhance plant productivity through various mechanisms such as CO₂ and nitrogen fertilization and an extension of the growing season in northern regions¹⁴. As a result, recently established forests have been documented to grow faster, with a greater carbon sequestration, than those established a century ago¹⁷. The expected, original carbon sink for a growing forest without environmental changes (which can be approximated by growth rates in pre-industrial times) is considered to be the direct management effect, whereas the 'extra' carbon sink brought about by improved growth conditions is considered to be the environmental effect.

In this study, the carbon-sequestration contribution of environmental changes in China was quantified using simulations from 17 DGVMs (TRENDY-v11) made available by the global carbon budget assessment¹⁵. The net biosphere productivity (NBP) driven by environmental changes over woody ecosystems was quantified using simulations from the S2 runs with transient forcings of climate, atmospheric CO₂ concentration and nitrogen deposition but without human land use change (that is, with a constant pre-industrial land cover distribution assuming mature ecosystems). For details, see https://github.com/RamAlkama/LandCarbonBudget_IntactAndNonIntactForest. Detailed information on the DGVMs is presented in Supplementary Method 3.

The annual mean NBP for 2001–2020, including both components for biomass and soil, was derived for each of the 17 different DGVMs and then re-gridded to a uniform spatial resolution of 0.5°. For each grid cell, the median of all 17 NBP values from the different DGVMs was calculated to represent the environmental effect. A spatial median filter with a 3 × 3 pixel window size was further applied to filter isolated pixels with extremely large NBP values.

Our satellite-derived carbon sink focuses on biomass only. However, only 13 of the 17 DGVMs provided the NBP separately for biomass and for soil. Hence, the ratio of the biomass NBP to the total NBP (both biomass and soil) was derived for each of the 13 DGVMs. The final ratio map, determined as the median of the 13 ratio maps (one for each of the 13 DGVMs) was applied to the median total NBP map of the 17 DGVMs (described above) to derive the biomass carbon sink caused by environmental changes.

The DGVM-based biomass NBP map was finally resampled to 500 m using the nearest neighbour method. DGVM-based biomass NBP data are for forest ecosystems. NBP values for woodland pixels at a 500 m scale were gap-filled as a certain proportion of the mean forest NBP in the window of 0.5 × 0.5° surrounding the target woodland pixel. The proportion was calculated as the ratio between the tree cover of the target woodland pixel and the mean tree cover of all forest pixels inside the 0.5 × 0.5° window.

Last, the direct management effect on the woody carbon balance was derived as the difference between the satellite-derived woody biomass carbon sink and the DGVM-based estimate of the environmentally driven sink for all land management types.

## Quantifying contributions to the management-induced woody carbon sink by major ecological restoration projects

In the late 1970s, in response to environmental degradation and sustainability challenges, the Chinese Government began to launch a portfolio of large-scale ecological restoration projects (Supplementary Table 2). Here we focused on forest and woodland ecosystems and quantified the contributions of several key ecological restoration projects to direct human management effects on the woody carbon balance in China.

Six projects were considered: the NFCP, the GGP, the SCP, the cluster of SFPs (which included the Three-North Shelter Forest Program, the Yangtze River Shelter Forest Program, the Zhujiang River Shelter Forest Program, the Coastal Shelter Forest Program and the Taihang

Mountain Greening Program), the FHTP and 'other projects' (other recorded anthropogenic afforestation and conservation activities that are not included in any of the above-mentioned projects).

Two major land management forms were adopted in these projects. One is anthropogenic afforestation through either manual planting or aerial seeding. The other is the more passive management of 'conservation' of forest and woodland, for example, mountain closure, which reduces biomass extraction by human beings by limiting access to woody ecosystems and thus contributes to ecosystem recovery and biomass growth. We assume that the anthropogenic afforestation effect of the ecological restoration projects corresponds to the afforestation and woodland expansion land management categories derived via remote sensing (Extended Data Fig. 5), and that the conservation effect resides in the stable forest and stable woodland categories. The direct-management-induced carbon sink for each ecological project was derived as the sum of both direct management effects of anthropogenic afforestation and the conservation of forest and woodland. Details of how we allocate the observed direct-management-induced carbon sink to different ecological projects are given in Supplementary Method 4.

## Data availability

All inventory data, such as forest cover, GSV, and anthropogenic afforestation areas established by ecological projects, among others, are available from the Chinese Forestry Statistical Yearbooks. The supporting datasets extracted from Chinese Forestry Statistical Yearbooks and the forest AGB data derived in this study are available via Figshare at https://doi.org/10.6084/m9.fishshare.25002029 (ref. 55). All MODIS data are freely available from Earthdata (https://search.earthdata.nasa.gov). The reference tree-cover and forest-loss year data from ref. 25 are freely available online at https://storage.googleapis.com/earthenginepartners-hansen/GFC-2021-v1.9/download.html. GEDI L4B is freely available from Oak Ridge National Laboratory Distributed Active Archive Center at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=2299. Forest root biomass data from ref. 46 are freely available via Figshare at https://doi.org/10.6084/m9.fishshare.12199637.v1. The environmental carbon-sink data as simulated by the 17 DGVMs are freely available via GitHub at https://github.com/RamAlkama/LandCarbonBudget_IntactAndNonIntactForest.

## Code availability

MATLAB (R2020b) was used to generate the results. The script of the BEAST algorithm is freely available from https://ww2.mathworks.cn/matlabcentral/fileexchange/72515-bayesian-changepoint-detection-time-series-decomposition?SID=srchtitle. All other analysis scripts are available from C.Y. upon request.

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## Acknowledgements

We thank the TRENDY DGVM community for making their model outputs available and Y. Zhang for CFSY data collection. This study was supported by National Natural Science Foundation of China (number U20A2090 to B.H. and numbers 42371346 and 42001292 to Z.L.) and by the Second Tibetan Plateau Scientific Expedition and Research Program (2022QZKK0101), the National Key Research and Development Program of China (2023YFB3907403) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB40000000) to C.Y.

## Author contributions

C.Y. designed the study. Z.L. performed the analysis. R.A. performed the analysis of the DGVM data. C.Y. and Z.L. draughted the paper with substantial help from K.Z. Interpretation of the results was assisted by B.H., P.C., G.G. and S.S. R.C., X.Q., M.X., M.W. and all other authors contributed to the writing.

## Competing interests

The authors declare no competing interests.

## Additional information

**Extended data** is available for this paper at
https://doi.org/10.1038/s41561-024-01569-0.

**Supplementary information** The online version contains supplementary material available at
https://doi.org/10.1038/s41561-024-01569-0.

**Correspondence and requests for materials** should be addressed to
Chao Yue or Binbin He.

**Peer review information** *Nature Geoscience* thanks Martin Brandt, Martin Jepsen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the *Nature Geoscience* team.

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<table><caption>Extended Data Table 1 | Separating the satellite-observed biomass carbon sink for 2001–2020 into environmental and direct management effects for different land management types (Tg C yr⁻¹)</caption><thead><tr><th></th><th>Area (Mha)</th><th>Total carbon sink (Tg C yr<sup>-1</sup>)</th><th>Environmental effect (Tg C yr<sup>-1</sup>)</th><th>Direct management effect (Tg C yr<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Afforestation</td><td>53.8</td><td>49.8</td><td>19.9</td><td>29.9</td></tr><tr><td>Woodland expansion</td><td>34.3</td><td>11.5</td><td>5.2</td><td>6.3</td></tr><tr><td>Stable forest</td><td>154.0</td><td>117.6</td><td>58.4</td><td>59.1</td></tr><tr><td>Stable woodland</td><td>18.0</td><td>6.0</td><td>2.1</td><td>3.9</td></tr><tr><td>Rotational forest</td><td>36.0</td><td>19.7</td><td>12.6</td><td>7.1</td></tr><tr><td>Rotational woodland</td><td>28.5</td><td>5.4</td><td>10.0</td><td>-4.5</td></tr><tr><td>Woodland clearing</td><td>3.6</td><td>-0.4</td><td>0.4</td><td>-0.9</td></tr><tr><td>Deforestation</td><td>2.6</td><td>-0.9</td><td>0.9</td><td>-1.8</td></tr><tr><td>Sum</td><td>330.8</td><td>208.6</td><td>109.5</td><td>99.1</td></tr></tbody></table>
<table><caption>Extended Data Table 2 | Biomass carbon sink induced by several key ecological restoration projects in China</caption><thead><tr><th rowspan="3">Project name</th><th colspan="6">This study</th><th>Lu et al.<sup>11</sup>, 2018</th></tr><tr><th colspan="2">Afforestation</th><th colspan="2">Forest conservation</th><th colspan="2">Sum</th><th rowspan="2">Sum<br>C sink (Tg C yr<sup>-1</sup>)</th></tr><tr><th>Area (Mha)</th><th>C sink (Tg C yr<sup>-1</sup>)</th><th>Area (Mha)</th><th>C sink (Tg C yr<sup>-1</sup>)</th><th>Area (Mha)</th><th>C sink (Tg C yr<sup>-1</sup>)</th></tr></thead><tbody><tr><td>NFCP</td><td>9.6</td><td>4.3</td><td>81.6</td><td>31.4</td><td>91.2</td><td>35.7</td><td>10.7</td></tr><tr><td>GGP</td><td>23.0</td><td>9.4</td><td>0.6</td><td>0.2</td><td>23.6</td><td>9.6</td><td>12.0</td></tr><tr><td>SFPs</td><td>12.7</td><td>4.1</td><td>2.2</td><td>0.5</td><td>14.9</td><td>4.6</td><td>16.8</td></tr><tr><td>SCP</td><td>3.1</td><td>1.4</td><td>1.1</td><td>0.4</td><td>4.2</td><td>1.8</td><td>5.8</td></tr><tr><td><strong>Subtotal</strong></td><td><strong>48.4</strong></td><td><strong>19.2</strong></td><td><strong>85.5</strong></td><td><strong>32.5</strong></td><td><strong>133.9</strong></td><td><strong>51.7</strong></td><td><strong>45.3</strong></td></tr><tr><td>FHTP</td><td>0.3</td><td>0.1</td><td>0</td><td>0</td><td>0.3</td><td>0.1</td><td>N.A.</td></tr><tr><td>Other projects</td><td>39.1</td><td>16.7</td><td>11.7</td><td>4.5</td><td>50.8</td><td>21.2</td><td>N.A.</td></tr><tr><td><strong>All projects</strong></td><td><strong>87.9</strong></td><td><strong>36.0</strong></td><td><strong>97.2</strong></td><td><strong>37.0</strong></td><td><strong>185.0</strong></td><td><strong>73.0</strong></td><td>N.A.</td></tr></tbody></table>

Only the direct management effect was shown here. Carbon sink caused by the direct management effect as reported in Lu et al.<sup>11</sup> did not separate for biomass and soil. For comparison with our results, their biomass carbon sink of direct management was calculated by applying the reported proportion of biomass sink to the total (biomass and soil) sink, including both environmental and management effects, on their reported carbon sink of direct management.
<figure><img src="image_16.png" /></figure>

**Extended Data Fig. 1** | China's forest cover dynamics (a) and forest area change slope (b) by different land cover products in comparison to those by NFIs. MODIS data is from Friedl et al.³⁶; CCI data is from ESA³⁷; GLASS GLC data is from Liu et al.⁵⁸; CLCD data is from Yang and Huang⁵⁹; GLC_FCS30 data is from Zhang et al.⁶⁰; and GLCNMO data is from Tateishi et al.⁶¹. The NFIs-reported

<figure><img src="image_17.png" /></figure>

forest cover growth in China agrees best with that derived in this study. Although GLCNMO also shows a growing trend for forest cover in China, it is less reliable since it has an overall low forest area. In addition, we found that it fails to accurately capture forest cover changes at provincial scale.
<figure><img src="image_18.png" /><figcaption>**Extended Data Fig. 2** | Flowchart of the methodology used in this study. Seven major processes were involved, including MODIS data preprocessing (a), tree cover mapping (b), change detection and piecewise fitting (c), biomass mapping</figcaption></figure>

(d), land use types classification (e), separating environmental and management effects on forest carbon sink (f) and quantifying contributions to carbon sink by major ecological restoration projects.
<figure><img src="image_19.png" /><figcaption>**Extended Data Fig. 3** | Relationships between estimated forest cover and NFIs-based forest cover. The comparison was conducted for different provinces across the four (6<sup>th</sup>-9<sup>th</sup>) individual inventory periods.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Extended Data Fig. 4** | Representative tree cover temporal dynamics for different land management types. Red dots represent piece-wise fitted tree cover. The blue dotted line represents the tree cover threshold for forest; the dark dotted line represents the tree cover threshold for woodland.</figcaption></figure>
<figure><img src="image_21.png" /></figure>

Extended Data Fig. 5 | Comparing the areas of afforestation plus woodland expansion derived by remote sensing in this study with the anthropogenic afforestation areas established through ecological restoration projects as reported by Chinese Forest Statistical Yearbooks over 1996–2018 for

different provinces. The point color and colorbar on the right show the mean annual precipitation for each province for 2001–2020. The province of Neimenggu was considered an outlier and excluded in the regression.
<figure><img src="image_22.png" /><figcaption>Extended Data Fig. 6 | Cumulative areas of established anthropogenic afforestation over 1996–2018 for different ecological restoration projects for different provinces. Blank areas indicate provinces that are not covered by a given project.</figcaption></figure>