# Global patterns and drivers of tropical aboveground carbon changes

Yu Feng, Philippe Ciais, Jean-Pierre Wigneron, Yidi Xu, Alan Ziegler, Dave van Wees, Arthur Nicolaus Fendrich, Dominick Spracklen, Stephen Sitch, Martin Brandt, et al.

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Yu Feng, Philippe Ciais, Jean-Pierre Wigneron, Yidi Xu, Alan Ziegler, et al. Global patterns and drivers of tropical aboveground carbon changes. Nature Climate Change, 2024, 10.1038/s41558-024-02115-x. hal-04710829

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# Global patterns and drivers of tropical aboveground carbon changes

Received: 20 November 2023

Accepted: 8 August 2024

Published online: 12 September 2024

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Yu Feng<sup>1,2</sup>✉, Philippe Ciais<sup>2</sup>, Jean-Pierre Wigneron<sup>3</sup>, Yidi Xu<sup>2</sup>,
Alan D. Ziegler<sup>4,5</sup>, Dave van Wees<sup>6</sup>, Arthur Nicolaus Fendrich<sup>2</sup>,
Dominick V. Spracklen<sup>7</sup>, Stephen Sitch<sup>8</sup>, Martin Brandt<sup>9</sup>, Wei Li<sup>10</sup>,
Lei Fan<sup>11</sup>, Xiaojun Li<sup>3</sup>, Jie Wu<sup>1</sup> & Zhenzhong Zeng<sup>1,12</sup>

Tropical terrestrial ecosystems play an important role in modulating the global carbon balance. However, the complex dynamics and factors controlling tropical aboveground live biomass carbon (AGC) are not fully understood. Here, using remotely sensed observations, we find a moderate net AGC sink of 0.21 ± 0.06 PgC yr⁻<fnref n="1" /> throughout the global tropics from 2010 to 2020. This arises from a gross loss of -1.79 PgC yr⁻<fnref n="1" /> offset by a marked gain of 2.01 ± 0.06 PgC yr⁻<fnref n="1" />. Fire emissions in non-forested African shrubland/savanna biomes, coupled with post-fire carbon recovery, substantially dominated the interannual variability of tropical AGC. Fire radiative power was identified as the primary determinant of the spatial variability in AGC gains, with soil moisture also playing a crucial role in shaping trends. We highlight the dominant roles of anthropogenic and hydroclimatic determinants in orchestrating tropical land carbon dynamics and advocate for land management to conserve indispensable ecosystem services worldwide.

Tropical terrestrial ecosystems encompass more than half of the global aboveground live biomass carbon<fnref n="1" /> (AGC) and are critical components of the global carbon cycle²⁻⁴. Even relatively minor changes in tropical AGC potentially have an important influence on global atmospheric CO₂ con- centration, subsequently modulating climate change³. Contemporary estimates of tropical AGC changes often hinge on broad assumptions, sparse ground data and/or inaccurate estimation methods⁶, leading to uncertainties in identifying tropical land carbon sources/sinks, which remain the most uncertain component of the global carbon budget²,⁷,⁸. Satellite observations have shown that deforestation and forest degrada- tion cause tropical forest AGC to be net carbon sources⁹, whereas others suggested that carbon sequestration by secondary forests resulted in

tropical forests being net carbon sinks⁴,¹⁰. All in all, a key research ques- tion for the tropical carbon budget is whether losses from current distur- bances in some regions are offset by recovery from past disturbances in other regions. Another complicating issue is that studies usually focus on forests⁴,⁶,⁹,¹¹,¹², ignoring tropical non-forested ecosystems that have a great influence on the trends and variability in the land carbon sink, with fire often playing a critical role in their AGC changes¹³⁻¹⁵.

Current limited understanding of the tropical carbon budget stems from several factors, including limitations in optical satellite-based forest AGC quantification¹⁶, inaccuracies of coarse satellite products that lead to erroneous quantification of forest disturbance and/or fire activities¹⁷,¹⁸ and an incomplete understanding of climate and

<footnote n="1">¹Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China. ²Laboratoire des Sciences du Climat et de l'Environnement, UMR 1572 CEA-CNRS-UVSQ, Gif-sur-Yvette, France. ³INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave-d'Ornon, France. ⁴Faculty of Fisheries Technology and Aquatic Resources, Mae Jo University, Chiang Mai, Thailand. ⁵Water Resources Research Center, University of Hawaii, Honolulu, HI, USA. ⁶BeZero Carbon, London, UK. ⁷School of Earth and Environment, University of Leeds, Leeds, UK. ⁸Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK. ⁹Department of Geoscience and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark. ¹⁰Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, China. ¹¹School of Geographical Sciences, Southwest University, Chongqing, China. ¹²School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China. ✉e-mail: yu.feng@lsce.ipsl.fr</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1 | Spatial and temporal patterns of AGC losses during 2010–2020 over the tropics.** a–d, Temporal patterns of AGC loss over the tropics (a), tropical America (b), Africa (c) and Asia (d). e–g, Spatial patterns of fire-non-forest (e), fire-forest (f) and non-fire (g) AGC loss. In e–g, the maps show the annual mean loss (MgC ha⁻¹ yr⁻¹) for the period 2010–2020. Fire-non-forest and fire-forest losses are fire emissions (quantified by van Wees fire emission products) in</figcaption></figure>

non-forests and forests (quantified by TMF products), respectively. In g, non-fire losses are forest degradation- and deforestation-induced AGC losses through non-fire means. The resolution of the maps is 0.25°. It should be noted that the subgrid actual changes of AGC at a finer scale are very heterogeneous with localized losses.

anthropogenic impacts on AGC changes¹⁹⁻²¹. The AGC maps used in previous studies¹,²,²³, despite their high spatial resolution (30–500 m), mostly provide static forest biomass or cannot quantify the trajectories of AGC across all terrestrial ecosystems. Developing robust and temporally consistent maps for quantifying AGC dynamics, accompanied by a comprehensive understanding of the controlling factors, is crucial in minimizing uncertainties in land carbon sinks/sources.

Products from the soil moisture and ocean salinity (SMOS) passive microwave satellite have proved useful in exploring AGC dynamics¹⁶.

The satellite delivers daily observations of L-band vegetation optical depth (L-VOD), a parameter related to the water content in vegetation mass. Previous studies have used L-VOD as an indicative proxy for spatially and temporally explicit assessments of AGC dynamics¹²,¹⁶,¹⁹,²⁰,²⁴. The L-VOD-based AGC allows reliable estimates of annual net changes at regional or continental scales¹⁶. One challenge in elucidating AGC dynamics with coarse-resolution L-VOD products is to disentangle the interplay between losses and gains occurring in individual grid cells. For example, in 25 km L-VOD grid cells, the processes causing
subgrid AGC losses (for example, local fire emissions¹⁷) and gains (for example, forest recovery after disturbance) might offset each other within a year, leading to an ambiguous attribution of overall drivers of AGC changes. Further, both carbon losses and gains are influenced by local climatic, edaphic, biotic and anthropogenic factors¹⁰,²⁵. The processes underlying AGC change, especially those governing AGC gains, are not well understood²⁶, forming an obstacle to the effective management and conservation of tropical ecosystems in alignment with climate change goals.

In this study, we integrated passive microwave and optical satellite observations to create annual, wall-to-wall maps of AGC stocks and the corresponding change processes across the tropics for 2010–2020. Our domain covers most of the tropical regions, excluding Australia and is further bounded by the extent of tropical moist forests²⁷ (TMF; Extended Data Fig. 1). Our objectives are to: (1) quantify the dynamic processes affecting the net AGC change in each L-VOD grid cell, specifically the gross losses and gains and (2) investigate the factors governing AGC gains. Combining L-VOD and optical satellite observations, we derived annual AGC net changes, gross losses and gains (Methods), with the processes and the definitions of the losses and gains provided in Extended Data Fig. 2. Subsequently, we used a boosted regression tree (BRT) model to identify the key factors that govern the spatial variability and trends of the AGC gains. The novelty in our study is in (1) the quantification of annual AGC dynamics over nearly all tropics; (2) the allocation of the loss to different processes (fire, deforestation and degradation); and (3) an attempt to quantify the complex drivers of all types of AGC gains.

## Patterns of AGC loss

The gross annual losses in AGC from 2010–2020 were quantified at –1.79 PgC yr⁻¹ (Fig. 1a). Fire emissions constituted the principal component of this loss, accounting for 62% of AGC losses (or –1.11 PgC yr⁻¹). The magnitude of the non-fire deforestation and degradation losses is slightly lower but comparable to previous estimates (Extended Data Table 1). Our study includes both forest degradation and deforestation but is lower than previous estimates mainly because: (1) our study domain has masked invalid L-VOD observations and non-TMF regions (Extended Data Fig. 1), particularly in many regions of mainland Southeast Asia with high AGC stocks²⁸; (2) our estimates of non-fire degradation and deforestation loss did not include fire-related forest disturbance; and (3) our study used forest activities and baseline AGC map to quantify AGC losses but other studies, for example, ref. 9, used annual AGC maps at a coarse spatial resolution to quantify the losses. Beyond this, our total loss considered the loss processes not only in forests but also in non-forested shrublands/savannas (inferred from fire emissions as these ecosystems are fire-prone²⁹). Thus, our estimates provide better information for the carbon budget. Overall, at the ecoregion scale (Supplementary Fig. 1), non-fire losses in moist broadleaf forests (–0.55 PgC yr⁻¹) and non-forest fire emissions in grasslands, savannas and shrublands (–0.80 PgC yr⁻¹) jointly accounted for 75% of the tropical losses.

Over time, losses attributed to non-fire–forest disturbances, including deforestation and forest degradation, have shown an upward trajectory, increasing (a more negative flux in Fig. 1a) at a rate of –0.36 ± 0.12 PgC yr⁻¹ per decade (*P* < 0.05). This increasing trend aligns with previous studies showing a doubling of tropical carbon loss from forest disturbance¹¹. AGC losses due to fires, encompassing both forested and non-forested areas, showed no trend with fluctuations, with fire emissions of non-forest regions (–1.08 PgC yr⁻¹) being markedly greater than those from forests (–0.03 PgC yr⁻¹).

Regionally in tropical America, losses resulting from non-fire–forest disturbances (–0.31 PgC yr⁻¹) marginally surpassed those of total fire emissions (–0.20 PgC yr⁻¹). Both types of losses were predominantly located in the Brazilian ‘arc of deforestation’. Notably, the AGC loss from non-fire disturbances penetrated further into the Amazonian

biome (Fig. 1g), predominantly caused by increasing forest conversion for agriculture and infrastructure development¹²,³⁰. Trends in non-fire loss estimates are also similar to previous independent estimates of AGC losses due to large-scale deforestation¹¹, as very large-scale clearing of trees is usually associated with non-fire³¹. AGC losses in tropical America were concentrated in the forests, whereas the loss hotspots of tropical Africa are in savannas/shrublands (Fig. 1e and Supplementary Fig. 2). Fire-induced AGC loss in African non-forest biomes represented 77% (–0.80 PgC yr⁻¹) of the total AGC losses in the continent (–1.04 PgC yr⁻¹). In contrast, non-fire–forest AGC losses in Africa, totalling –0.24 PgC yr⁻¹, were predominantly concentrated in wet northern and southern regions of the Congo Basin and the coastal belts of West Africa, mainly in moist forests. AGC losses in tropical Asia were smaller than in the other two continents, a result that must consider that tropical Asia has the smallest study domain. Non-fire disturbances accounted for –0.14 PgC yr⁻¹, slightly surpassing AGC losses related to fire (–0.11 PgC yr⁻¹). Most fire-related carbon losses, especially in non-forested areas, were in mainland Southeast Asia, notably Cambodia and Myanmar. Conversely, forest AGC losses were more predominant in Indonesia, a historical deforestation hotspot.

Losses of AGC due to deforestation and forest degradation play a pivotal role in influencing terrestrial biomass carbon reservoirs. Previous studies have detailed the magnitude of AGC loss attributed to deforestation⁴,⁶,¹¹. However, specific losses stemming from forest degradation have been rarely assessed³². Forest degradation induces AGC loss but without producing discernable land cover change³³. While reasonably accurate mapping of these types of degraded forests and their degradation processes can be achieved with very high-resolution satellite observations³⁴, long-term and global datasets are unavailable at these resolutions. In our estimates, we use the TMF products to discern that degradation plays a slightly more influential role than deforestation in forest AGC loss (Supplementary Fig. 3). Across the tropical regions, forest degradation was responsible for 57% of the total forest AGC losses, a finding in agreement with other studies suggesting that forest degradation losses in tropical regions exceed those of deforestation, despite the lower AGC losses per area²⁴,³⁰. However, the interplay between deforestation and degradation may vary over time with climate conditions³⁵,³⁶.

## AGC net changes and gains

The estimated net change in AGC from 2010 to 2020 was 0.21 ± 0.06 PgC yr⁻¹ (mean ± s.d. of the 18 estimates using different L-VOD AGC calibration; Fig. 2a,e). This value indicates that tropical lands acted as a moderate carbon sink in AGC during the last decade. We also observed a sustained carbon source from 2011 to 2016 over the tropics, including the impact of the major drought during 2015/2016, followed by a subsequent phase of carbon recuperation after the drought (Extended Data Fig. 3). A previous study asserted that tropical AGC had not fully recovered to its predrought levels of 2014 by the end of the year 2017¹⁹. Our extended dataset shows that AGC has fully recovered by the end of 2020.

Upon deducting AGC losses from the AGC net changes based on L-VOD, we derived an estimation for the AGC gains. In most cases, the AGC gains indicated a carbon increase (positive sign) from the recovery from past disturbances and possibly enhanced growth of ecosystems from environmental factors such as rising CO₂ or regional wetting trends. However, a few pixels (9% of the total) of the AGC gain map show a negative sign, which indicates that, locally, the gains were offset by losses that were not explicitly accounted for by our loss model, for example, from drought-induced dieback, increased harvest and so on (Fig. 2f). Across the entire tropics, the AGC gains represented a sink averaging 2.01 ± 0.06 PgC yr⁻¹ from 2010 to 2020. At ecosystem scales, AGC gains were highest in the ecoregion of grasslands, savannas and shrublands (51% of the gains in the tropics or 1.02 ± 0.03 PgC yr⁻¹), followed by gains in moist broadleaf forests (38%; 0.76 ± 0.01 PgC yr⁻¹).
<figure><img src="image_4.png" /><figcaption>**Fig. 2 | Spatial and temporal patterns of AGC changes during 2010–2020 over the tropics.** *a–d*, Temporal patterns of AGC changes over the tropics (*a*), tropical America (*b*), Africa (*c*) and Asia (*d*). *e–g*, Spatial patterns of AGC net changes (*e*), gains, that is, total gains (*f*) and total losses (*g*). The maps show the annual mean values (MgC ha⁻¹ yr⁻¹) for 2010–2020. Negative values indicate loss of AGC, while positive values show gain of AGC. *In a–d*, the solid green lines indicate the mean</figcaption></figure>

of AGC gains and shaded areas represent ±1 s.d. among the 18 estimates. *In e–g*, the resolution of the maps is 0.25°. It should be noted that the subgrid actual changes of AGC at a finer scale are very heterogeneous with localized losses from disturbances and very different values of AGC across landscapes reflecting regrowth from past disturbance patches at landscape scale.

The AGC gains demonstrated a temporal variability that closely mirrored AGC net changes (Fig. 2a). This strong correlation between AGC gains and net changes, with a correlation coefficient (r) of 0.98 (P < 0.01), underscores the critical role of AGC gain in determining the interannual variability of AGC net changes. AGC losses are primarily induced by anthropogenic activity and fires that do not vary greatly from year to year²⁹. Thus, the interannual variability of AGC losses was smaller than that of AGC gains, leading to a more important role of AGC gains in dominating the interannual variability of net AGC changes.

In tropical America, the most prominent AGC gains were detected in the 'arc of deforestation' and southern Brazil, where substantial AGC

losses and secondary forest regrowth have been previously recorded. The AGC gains of tropical America constituted 27% (0.54 ± 0.02 PgC yr⁻¹) of the AGC gains across the tropics. Tropical Africa emerged as the predominant contributor to tropical AGC gains, contributing to 58% or 1.17 ± 0.03 PgC yr⁻¹. The regions that contributed to these gains include the Central African Republic, eastern parts of South Sudan, Angola, Zambia and Tanzania. These regions, typified by shrublands and dry forests (Supplementary Fig. 2) and moist forests in southern Central African Republic, only constitute 16% of tropical lands but totalled 35% of tropical AGC gains (0.71 ± 0.01 PgC yr⁻¹). Tropical Asia only witnessed modest AGC gains, contributing merely 15% or
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**Fig. 3 | Factors influencing spatial variability and trends in AGC gains during 2010–2020 over the tropics.** a, Relative importance of the variables in explaining the spatial variability of AGC gains. b–e, Partial dependence plots of the top four variables: fire radiative power (FRP; b), fraction of tree ($F_t$; c), young regrowth (d) and shortwave solar radiation ($R_s$; e), in explaining the spatial patterns of AGC gains. Grey lines are the 30 individual estimates and red lines are the median (Methods). The unit for the y axis of AGC gains is MgC ha⁻¹ yr⁻¹.

f, Relative importance of the variables in explaining the trends in AGC gains. g–j, Partial dependence plots of the top four variables: soil moisture trend (δSM; g), shortwave solar radiation trend (δ$R_s$; h), shortwave solar radiation ($R_s$; i) and livestock (j), in explaining the trends in AGC gains; δ denotes the trends in time series. In j, M denotes million. The unit for the y axis of δAGC gains is MgC ha⁻¹ yr⁻². There are 19 and 23 variables explaining the spatial variability and trends in AGC gains, respectively, and only the top ten variables are listed in a and f.

0.30 ± 0.01 PgC yr⁻¹ to the tropical AGC gains. The predominant locations for these gains include Cambodia, central Sumatra and the coastal regions of Kalimantan.

Spatially, the negative correlation (*r* of −0.82, *P* < 0.01; Supplementary Fig. 4) between AGC losses and the AGC gains (Fig. 2) implies that pixels characterized by greater AGC losses were also typically associated with higher AGC gains. Fires and other disturbances may clear the overlying vegetation biomass but do not destroy the seed bank or underground root systems of plants that are able to resprout, facilitating rapid postdisturbance recovery and regrowth. Therefore, despite short-term disturbances, vegetation may regenerate quickly under suitable environmental conditions. Regionally, this relationship was pronounced in tropical Africa, where the principal driver of AGC loss was from non-forest fires, often in shrubland/savanna ecosystems. Regions where AGC losses were not fully accounted for included the Amazon Basin, northern Paraguay, the Congo Basin and Central Kalimantan, suggesting limitations of L-VOD in these critical regions.

Our analysis may have underestimated some of the AGC losses in regions predominantly characterized by old-growth forests subject to degradation and drought, specifically in the grid cells with negative values of the AGC gains (Fig. 2f). This under-representation could be attributed to limitations of the Landsat-based TMF data that may miss some degradation events³⁴, particularly small-scale features and/or events in cloudy regions, a situation potentially exacerbated by saturation and cloud effects¹⁶. Regarding small-scale features, earlier studies posited that factors such as timber extraction, fires, edge effects, selective logging and extreme drought caused substantial degradation³⁷. Small-scale disturbances, which escape detection by Landsat observations, probably contributed to an underestimation of AGC loss in our loss model. Another potential underestimation relates to the handling of unburned biomass left on the ground as coarse woody debris, which is recorded as an AGC loss based on L-VOD but is not included in our fire emission calculations using the fire emission products²⁹. Again, these types of ‘error’ may lead to an underestimation of total AGC losses.
Despite the potential underestimation of AGC losses, our new dataset could quantify the variability in annual AGC net changes in undisturbed forests (Supplementary Figs. 5 and 6). Further comparison of L-VOD with inventories in old-growth forests also confirmed the reliability of L-VOD in detecting old-growth forest changes³⁸, providing additional insights into the dynamics of undisturbed forests that cannot be detected well by current optical satellites and models. These multiple lines of evidence further underscore the invaluable role of L-VOD data in identifying subtle AGC changes.

## Factors influencing AGC dynamics

The spatial variability of the average AGC gain is predominantly governed by fire radiative power (FRP), accounting for >40% of the importance (Fig. 3a). As previously indicated, elevated AGC gains are commonly associated with high AGC losses (Supplementary Fig. 4) and are attributed to substantial carbon gains during the postdisturbance recovery. The partial dependence plot further elucidates this relationship, illustrating a near-linear dependence between AGC gains and FRP (Fig. 3b). Examining the bivariate map contrasting AGC gains with FRP highlights tropical Africa, particularly regions in tropical South Sudan, southern D.R. Congo and northern Angola, as focal points exhibiting elevated values for both AGC and FRP (Extended Data Fig. 4). However, forest regrowth inferred from TMF data in these regions of Africa was relatively limited, a finding that might signify a pivotal role of a post-fire rebound in non-forested shrub and woody savanna in constituting the AGC gains. Additionally, savanna ecosystems have high FRP and fast regrowth, indicating their ability to resprout quickly and evolution to cope with fire. Regional partial dependence plots (Extended Data Fig. 5) further substantiate this observation. While the area fraction of trees ($F_t$) emerges as an important determinant impacting the spatial variability of the AGC gains, its limited influence (9% to the overall importance) is much smaller than that of FRP. Further, the sensitivity of AGC gains to $F_t$ changes is near-linear, albeit considerably reduced compared to FRP (Fig. 3). Further, young forest regrowth is also an influential component, given its rapid carbon accumulation rate. This phenomenon is particularly pronounced in regions spanning West Africa, the Congo Basin and Indonesia.

Regarding the factors explaining the trends in AGC gains (δAGC gains; δ denotes the trend in time series), the change in soil moisture (δSM) emerged as the principal determinant, yet contributing only to a modest 10% of the importance for the trend. Generally, there is a positive correlation between δAGC gains and δSM. Notably, regions such as the northern Caatinga of Brazil, as well as parts of Kenya and Tanzania, benefited from upward trends in SM, which positively impacted AGC gains (Extended Data Fig. 6). Regarding shortwave radiation ($R_{\text{sw}}$), trends (δ$R_{\text{sw}}$) ranked as a secondary influential factor but with very few regions dominated by $R_{\text{sw}}$ dynamics. In addition to the trend, the absolute magnitude of $R_{\text{sw}}$ was also a vital factor influencing δAGC gains, exerting a predominantly negative effect. Particularly in regions north and south of the Congo Basin, as well as in South Sudan, elevated levels of $R_{\text{sw}}$ negatively impacted the trends of AGC.

Our results indicate that fire is the dominant factor regulating the spatial variability of AGC gains, that is, through post-fire recovery, mostly in African savannas and shrublands. Fire per se is not a negative driver of AGC change in these regions but an important ecosystem process that shapes vegetation/carbon dynamics and maintains biodiversity. However, human activity has altered fire regimes³⁹,⁴⁰, reshaping ecosystem succession and affecting the fraction of trees versus grasses in certain areas. As for δAGC gains, they are dominated by δSM, indicating that water limitations largely dictate the resilience and recovery of tropical vegetation and carbon dynamics, concurring with existing work positing that vegetation growth is increasingly tethered to water constraints in a progressively warming climate⁴¹, with pronounced effects observed in semi-arid ecosystems⁴².

In conclusion, our research offers a nuanced spatial and temporal quantification of AGC changes over the tropics, detailing both loss and gain and associated governing factors. These findings enrich our understanding of aboveground carbon dynamics, highlighting the intricate balance between climatic, biotic and anthropogenic factors that regulate land carbon accumulation. These insights make it imperative to address the detriments of ongoing emissions to guarantee the longevity of carbon-rich ecosystems. Notwithstanding, the postdisturbance gains observed in certain locales offer a glimmer of optimism despite growing pressure on global biomes. We advocate for formulating and implementing effective policies and practices in land management to conserve indispensable ecosystem services.

## Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41558-024-02115-x.

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# Methods

## Study region

Our study is geographically confined to the tropical regions situated between latitudes 23.5° N and 23.5° S, except northern Australia. We have inferred forest activities from the TMF²⁷ dataset. The domain of the TMF dataset covers various global ecological zones such as 'tropical rain forest', 'tropical moist forest', 'tropical mountain system' and 'tropical dry forest' following the ecozones definition of FAO global ecological zones classification systems. As a result of this reliance on the TMF dataset, we further excluded regions—predominantly in northern Africa, southern Africa and China—where TMF data are unavailable, as illustrated in Extended Data Fig. 1. The final study domain is considerably larger than the previous studies¹⁰ focusing on tropical rainforests. Our study includes various ecoregions of tropical rainforests, tropical dry forests, grasslands, savannas, shrublands and mangroves (Supplementary Fig. 1).

## AGC maps

Existing AGC maps, usually at high spatial resolution from 30 to 1,000 m, are static and only represent AGC in forests/woody vegetation for a single year, hindering the analysis of terrestrial AGC dynamics for a longer period. In this study, we used low-frequency L-VOD observations from the SMOS satellite (SMOS-IC v.2)43,44 to produce spatially and temporally consistent annual maps of terrestrial AGC, to quantify the AGC dynamics for 2010–2020. The L-VOD data are at a 25 km spatial resolution and we resampled the data to 0.25° spatial resolution for computational convenience, using the nearest neighbour method. The methodology for deriving AGC from L-VOD aligns with the approaches delineated in previous research16,19,20,24,38. Initially, we used benchmark AGC maps to calibrate the relationship between annual L-VOD measurements and AGC using a widely adopted model45, thereby ensuring methodological continuity and comparability with existing studies:

$$AGC = a \times \frac{\arctan(b \times (\text{L-VOD} - c)) - \arctan(-b \times c)}{\arctan(b \times (\text{Inf} - c)) - \arctan(-b \times c)} + d \quad (1)$$

where a, b, c and d are fitting parameters; L-VOD is annual L-VOD.

Following previous studies16,19,20, we used the yearly L-VOD data for 2011 and AGC values from six benchmark maps to calibrate equation (1) and derived the best fit for a, b, c and d; then, we implemented the fitted parameters and L-VOD data to generate spatially and temporally consistent AGC maps for 2010–2020. The six AGC benchmark maps include the Avitabile map46, Baccini map22, European Space Agency Climate Change Initiative (ESA-CCI) 2010 map¹(v.4), two Jet Propulsion Laboratory maps47,48 and the map of ref. 23. Some maps provided aboveground biomass (AGB) values rather than AGC. Thus, AGB (measured in Mg of biomass ha⁻¹) was converted to AGC (in MgC ha⁻¹) using a conversion factor of 0.5 (refs. 11,47), where AGC = AGB × 0.5.

We conducted the calibrations of equation (1) using the six AGC benchmark maps in the tropical region, tropical America and tropical Africa, respectively, resulting in 18 groups of calibrations. Each calibration, for example, using the Avitabile map in tropical America, generates one group of fitting parameters (that is, a, b, c and d in equation (1)), resulting in 18 functions in total. We excluded tropical Asia in the regional calibration because of the limited availability of L-VOD observations in that area. We subsequently used the 18 functions, equation (1) with 18 groups of fitting parameters, with annual L-VOD data from 2010–2020 to create 18 L-VOD-based annual AGC maps. We synthesized the mean of our 18 maps to produce an annual synthesis map of AGC change from 2010–2020, with the ±1 s.d. from the 18 estimates being used to report its uncertainty (Fig. 2)¹⁶. We further derived the maps of AGC net change using the final AGC maps at an annual interval, with net changes being calculated as the difference between AGC in each year to that of the previous year at the L-VOD pixel level of 0.25°. Because L-VOD observations are of low quality in water bodies, we masked all

L-VOD pixels where regularly flooded wetlands constituted more than 80% of the area²⁰. Additionally, pixels showing evident anomalies (such as an increase of >20 MgC ha⁻¹ in year-to-year AGC changes) were also masked, which aligns with a previous study¹².

We only used L-VOD for 2011 to calibrate equation (1) because 2011 was the first year with complete SMOS observations¹⁶. The six AGC maps are for different years and do not match the year of L-VOD, which might result in uncertainty. However, the variability of AGC stocks in different years is very small relative to the total stocks at the regional scale, that is, at 0.25°. So, the uncertainty caused by the mismatch in years is minimal at the regional scale. A previous study¹⁶ also found that the year selected for the calibration has a marginal impact on the L-VOD-derived AGC results.

To evaluate our L-VOD-based AGC maps, we compared the map with the AGC map of the Brazilian Amazon created by ref. 49. Overall, the two maps demonstrate congruent spatial distributions and exhibit a significant positive correlation ($r = 0.87, P < 0.01$; Supplementary Fig. 7). The spatial discrepancies, particularly in regions such as Amapá, can probably be ascribed to differing map foci: the carbon map of ref. 49 is forest-centric, whereas our L-VOD map encompasses all vegetation types. We also compared the annual AGC fluxes (the annual net changes of AGC) with the global atmospheric CO₂ growth rate (CGR), which integrates all carbon fluxes exchanged to and from the atmosphere. Although the CGR estimates are a global indicator, we found a strong correlation between CGR and annual AGC fluxes ($r = 0.81, P < 0.01$; Supplementary Fig. 8), aligning with the previous consensus that the interannual variability in CGR is dominated by tropical terrestrial ecosystems¹⁶,⁵⁰. We also observed differences between the AGC fluxes and CGR, which might be explained by the fact that CGR estimates include ecosystem respiration and carbon fluxes outside tropical regions³⁸. Overall, our newly developed AGC maps show patterns and magnitudes that are consistent with those of existing static AGC maps and they exhibit similar interannual variability when compared to atmospheric data. This consistency underscores the validity of our maps in accurately estimating terrestrial AGC.

## AGC losses from fire and non-fire–forest changes

The total AGC losses were calculated by aggregating the non-fire–forest changes (specifically, deforestation and degradation) and fire emissions.

**Fire emission.** Fire-induced AGC emissions were sourced from the 500 m fire emissions model by ref. 29 (hereafter van Wees fire emission products). This dataset was based on the global fire emissions database (GFED) modelling framework. It delivers comprehensive data on total biomass burning carbon emissions from aboveground and belowground sources, rendered at a 500 m spatial resolution and on a monthly basis spanning 2002–2020. For our analysis, we used the total biomass burning carbon emissions solely from aboveground sources to quantify AGC loss attributable to fire. The burned area (BA) in the ref. 29 product is largely based on MODIS MCD64A1 C6, complemented by additional burned area derived from the overlap of forest loss and active fire detections as part of the fire-related forest loss algorithm. A recent study showed that the BA in the MCD64A1 product was underestimated compared with the latest GFED5 BA product, resulting in lower fire emissions because of underestimating small fires³¹. We, thus, corrected the ref. 29 fire emission product by introducing a regional conversion factor to match the more recent GFED5. Especially in each GFED region, we estimated the regional ratio of GFED5/GFED4s BA (Table 5 of ref. 51) and used the ratio as a conversion factor to correct the ref. 29 fire emissions. Additionally, we used the BA data to differentiate fire emissions between forest and non-forest regions by overlaying the fire emission layer with the TMF forest change layer.
**Non-fire deforestation and degradation.** We used TMF data²⁷ to quantify deforestation and forest degradation. The TMF data leverage long-term Landsat satellite time series to quantify changes in forest cover in TMF. These data capture the dynamic changes in forests and provide a detailed narrative of forest transitions. We used two comprehensive and wall-to-wall TMF maps to quantify deforestation and forest degradation. According to TMF definitions, deforestation refers to 'the conversion of land cover from forested to non-forested land'. Forest degradation is considered 'a temporary disturbance in a forest that remains forested postdisturbance'. This degradation is often associated with disturbances such as fires, selective logging and extreme, unusual weather events. Through these classifications, TMF data offer a unique perspective for deeply understanding the forest changes in the tropics, providing valuable insights into the health and recovery potential of forest ecosystems. Comparison of the TMF with other existing products, for example, PRODES⁵², showed that, although there are differences between the TMF and PRODES, they show a similar decrease in annual deforestation rates for the twenty-first century. The differences are mainly caused by the mapping method: (1) TMF has a minimum mapping unit of 0.09 ha while PRODES has 6.25 ha; (2) TMF considers large fire patches as deforestation while PRODES does not; (3) PRODES only captures new, clear-cut deforestation of primary forest. Regarding the degradation mapping, we did not find any satellite products other than TMF that map the annual degradation events for the past decade at continental or tropical scales, which hinders the comparison of TMF with other products. Although the TMF dataset has uncertainties in quantifying deforestation and degradation, it represents the best option at present. Extensive studies applied the dataset to study tropical forest dynamics and their associated biogeochemical and biogeophysical impacts¹⁰,¹²,³³, helping to guide future environmental policies and practices towards sustainable development.

The TMF dataset, initially at 0.0002695° spatial resolution (-30 m at the Equator), was resampled to 0.00025° (-27.8 m at the Equator) for computational expediency. Annual deforestation was directly determined from the TMF pixels marked as 'deforested land'. To avoid double-counting the loss of fire emissions with loss from deforestation, we excluded fire-induced deforestation by masking all deforestation pixels that had been burned, as identified in the burned area layer of ref. 29. We did not use the land cover information (MODIS MCD12Q1 land cover⁵³) in the ref. 29 burned area products to separate forest- and non-forest-related burned area because the MCD12Q1 has different forest classification than the TMF forest mask. The AGC loss from deforestation was then calculated by overlaying the deforestation map (resampled to 0.00025°) with the AGC benchmark map. In this regard, we exclusively used the ESA-CCI 2010 AGC map to ascertain AGC loss from deforestation. The ESA-CCI map describes the woody land AGC for the year 2010, which can better quantify the forest AGC at a fine scale in 2010, the starting year of our study. The remaining AGC maps describe AGC in other years (for example, the Baccini map for 2007–2008). Thus, the time-match between the ESA-CCI map and the starting year of our study may reduce the uncertainty in forest AGC quantification. The ESA-CCI maps have been widely used for quantifying tropical forest AGC and have proved the feasibility of quantifying forest AGC in 2010¹⁰,³³.

We focused on forest AGC loss rather than on the carbon emissions from non-fire-forest disturbance. Thus, we used committed AGC loss for forest disturbance, meaning that all AGC is immediately assumed to be lost (not emitted to the atmosphere) after disturbance, without considering the legacy effects of carbon emission, such as from deadwood decay or conversion to wood products of variable lifetimes. This assumption is grounded in the nature of L-VOD, which is related to the water content of vegetation mass; once the trees are cut down, L-VOD no longer detects the water content and considers the corresponding AGC lost, even if stems and branches remain on the ground. So, we assume that AGC was completely and immediately

lost after the forest disturbance. Overall, we focused on the AGC loss from deforestation and degradation, not the carbon emission from deforestation and degradation.

We divided forest degradation into two distinct types: edge forests and other forms of degradation. To detect edge forests, we began by generating annual forest/non-forest maps using the TMF data, where pixels marked as 'undisturbed TMF', 'forest regrowth' and 'degraded TMF' were classified as forest and all other TMF pixels were designated as non-forest. Using a connected-component analysis, we determined the edge of each forest patch and set an edge depth of 100 m (approximately four TMF pixels or 0.001° at the Equator; ref. 54). Subsequently, we identified the age of edge forests and created annual maps reflecting this variable. Recognizing that the AGC loss rate from edge effects is influenced by forest edge age, we used the AGC loss rates (a function of edge age) from ref. 30 (Supplementary Fig. 9) in conjunction with ESA-CCI AGC, to compute AGC loss stemming from edge effects.

For other forms of degradation, such as those related to selective logging, insect damage and extreme weather, we first produced non-fire degradation maps by aligning the degradation layer of TMF with the ref. 29 BA layer. Following ref. 33, we first derived local estimates (in each 0.25° pixel) of the mean AGC differences between degraded and undisturbed forests for the year 2010, using both TMF and ESA-CCI AGC maps. Then, the AGC difference was taken as the degradation-induced AGC density loss locally in each 0.25° pixel and we further calculated the AGC loss from non-fire degradation each year at 30 m level, by combining the local degradation-induced AGC density loss with the TMF degraded forest map. As clarified previously, we also used the ESA-CCI map only for degraded and undisturbed forest AGC quantification for 2010³³.

## AGC gains

The net changes in AGC comprised both the gross AGC loss, resulting from fire and forest loss and the gross AGC gain from vegetation growth. Owing to the coarse spatial resolution of L-VOD, these losses and gains might offset each other, making it a challenge to distinguish between the gross loss and gain from the net changes.

To address this issue, we first calculated the gross AGC loss (AGC<sub>loss</sub>) that included both the fire emissions (sourced from ref. 29 product) and the non-fire emissions (that is, AGC loss from non-fire deforestation and forest degradation). We then subtracted this AGC<sub>loss</sub> value from the AGC net changes (AGC<sub>net</sub>), arriving at the residuals (AGC<sub>res</sub>). The residual component reflects the remaining changes in AGC after accounting for losses, and it can provide insights into the gross AGC gains from processes such as vegetation growth:

$$AGC_{res} = AGC_{net} - AGC_{loss} \qquad (2)$$

The AGC<sub>res</sub> was identified as a complex interplay of various processes, including undetected changes in old forests, non-fire shrub loss and, predominantly, forest/shrub growth (Extended Data Fig. 2). Among these, forest/shrub growth serves as the most important component, allowing AGC<sub>res</sub> to function effectively as a proxy for AGC gains in the analysis. Although AGC changes in croplands and grasslands are detected by AGC<sub>net</sub>, they are excluded from this calculation. The reason for this exclusion lies in the scarcity of relevant data and the minimal impact of these areas on overall AGC stocks, compared with forests and shrublands. Consequently, AGC<sub>res</sub> stands as a focused indicator of AGC gain, emphasizing the dynamics in forests and shrublands while disregarding less influential areas like croplands and grasslands. In our study, unless stated otherwise, we used AGC gains rather than AGC residuals for simplicity.

Most of the inputs for AGC loss analysis are at 30 m spatial resolution and we calculated the results at 30 m spatial resolution but summarized the final results of losses at 0.25° cells (mainly in spatial analysis) for computational expediency and better visualization of the
results. We calculated AGC changes (losses and gains) at 0.25° spatial resolution (Figs. 1 and 2). At this coarse spatial resolution, changes may appear homogenous regionally. However, it should be noted that the subgrid actual changes of AGC at a finer scale are very heterogeneous with localized losses from disturbances and very different values of AGC across landscapes reflecting regrowth from past disturbance patches at landscape scale—see, for instance, a nice illustration of heterogeneity in a small forest region⁵⁵.

## Factors controlling AGC gains

We used a BRT model to elucidate the factors shaping the spatial and temporal patterns of AGC gains. In this part, we only analysed the gains but ignored the losses due to two reasons: (1) the drivers of AGC losses, mostly related to anthropogenic activities, have been extensively quantified in previous studies, for example, ref. 11; (2) we have already explicitly modelled losses at high resolution and allocated the losses to different processes, for example, fire, non-fire–forest degradation and deforestation, to quantify how these processes, mostly human-driven, shape the losses. The gains of tropical AGC have been rarely studied, possibly because annual AGC gains at continental scale are difficult to map. Grounded in the proxies for AGC gains, we can further quantify the factors governing AGC gains.

Previous studies found that climatic, anthropogenic, edaphic and biotic factors regulate the terrestrial AGC dynamics²⁰,²¹. Here, we have added more factors related to long-term forest dynamics because our study focuses on the dynamics of the AGC gain changes over one full decade. Finally, we selected surface air temperature ($T_a$), shortwave solar radiation ($R_s$), vapour pressure deficit (VPD), soil moisture (SM), mean annual area of forest fire for the past 10 years, human footprint (HFP), livestock density, areas of young (≤10 years) and old (>10 years) plantations, areas of young (≤10 years) and old (>10 years) forest regrowth (young and old regrowth, respectively), soil clay content, soil organic carbon stocks, total soil nitrogen, vascular plant species, area fraction of trees ($F_t$), shrublands ($F_s$) and grasslands/croplands ($F_g$). The area of young and old secondary forests was gleaned from the TMF dataset, using the methodologies of ref. 56, whereas the other variables were sourced from publicly available online repositories. Comprehensive details on these variables are catalogued in Supplementary Table 1.

The BRT model is an advanced, widely used technique for addressing regression problems, leveraging an ensemble of decision trees for predictive insights⁵⁷. It operates by iteratively augmenting new trees to the model to reduce the gains or the error of the model, an approach known as ‘boosting’. One of the model’s key strengths is its ability to assign variable importance, quantifying the contribution of each explanatory variable to the model’s predictions. A higher variable importance signifies a more pronounced influence of the explanatory variable on the response variable (in this case, AGC gains), allowing for a nuanced analysis of the underlying relationships.

To model the spatial patterns of AGC gains, we used the mean annual values of the explanatory variables, incorporating 19 such variables. To model trends in AGC gains, we considered the trends in time-series variables, the mean values of climate variables and static variables, encompassing 23 explanatory variables. A comprehensive list of these variables, pertinent to modelling both spatial and temporal patterns of AGC gains, is delineated in Supplementary Table 1.

Our modelling approach adopted a hold-out validation strategy, in which 70% of the dataset was used for training the BRT model and the remaining 30% was reserved for validation. For the parameterization of the BRT model, a trial-and-error method was deployed to ascertain the optimal value for the ‘number of trees’ parameter. This was achieved by incrementally varying it from 100 to 1,000 in intervals of 100. The model’s performance was observed to be augmented with the parameter and reached a plateau beyond the value of 500. Consequently, we selected 500 as the value for the ‘number of trees’ in the final BRT model. To elucidate the nuanced relationships between the target

variables (AGC gains and δAGC gains) and their four primary explanatory variables (FRP, $F_t$, young regrowth and $R_s$ for AGC gains; and δSM, $δR_s$, $R_s$ and livestock density for δAGC gains), we used an analytical approach using a bootstrapping methodology. In this framework, for each respective explanatory variable, a subset of 70% of observations falling in the 5th to 95th percentile range of the explanatory variable was randomly selected to derive the partial dependency curve. This procedure was systematically replicated 30 times. After these iterations, both the mean and individual estimates were harnessed for further analysis. The BRT model can elucidate 71% of the spatial variations and 41% of the variations in the temporal trend of the AGC gains (Fig. 3). The model also adeptly mirrored the spatial distributions of the AGC gains (Supplementary Fig. 10) and its associated trends (Supplementary Fig. 11), including predicting the signs (positive or negative) of the AGC gains.

The BRT model reveals SM as the dominant factor governing the trends in AGC gains. To test the robustness of SM, we further compared the ERA5-Land SM with an independent global SM product⁵⁸ (Supplementary Fig. 12) and found that ERA5-Land positively correlated with the independent SM product, confirming the increasing SM observed from the ERA5-Land product in the abovementioned regions and its role in governing the trends in AGC gains.

## Caveats and limitations

Our study has enhanced the comprehension of AGC dynamics across the tropics over the past decade. Nonetheless, there are caveats and limitations to note. The research time-frame spans from 2010 to 2020, precluding extension to more recent periods, such as up to 2023. A primary challenge lies in the scarcity of updated auxiliary data. Certain auxiliary datasets, such as the TMF and L-VOD data, are refreshed annually. However, the ref. 29 fire emissions data only extend to 2020, constraining our ability to analyse more recent trends or to conduct ‘real-time’ monitoring of AGC dynamics, that is, annual updates. A potential solution for ‘real-time’ tracking annual AGC dynamics could involve downscaling L-VOD-based AGC maps from a 25 km resolution to a finer scale, for example, 100 m. This approach would directly quantify AGC gains and losses in various ecosystems. Another limitation of our study is related to the successional dynamics of undisturbed forests. Our algorithm could describe the variability in AGC net changes in undisturbed forests (Supplementary Figs. 5 and 6) but failed to quantify their successional dynamics. The downscaled L-VOD-based AGC maps with in situ and auxiliary forest data could provide more information on the successional dynamics of undisturbed forests.

## Data availability

The L-VOD data are available at the SMOS-IC website (https://ibremote-sensing.inrae.fr/). The TMF data are available at https://forobs.jrc.ec.europa.eu/TMF. The van Wees fire emission data were downloaded from ref. 29 (https://zenodo.org/records/7229675). The GFED5 burned area data are available via Zenodo at https://doi.org/10.5281/zenodo.7668423 (ref. 59). The ESA-CCI AGB data are available at https://climate.esa.int/en/odp/#/project/biomass. The Zarin AGB data are available at https://data.globalforestwatch.org/datasets/3e8736c8866b4586867e00d40c9f00bce_0/about. The Avabile AGB data are available at http://lucid.wur.nl/datasets/high-carbon-ecosystems. The Baccini AGB data are available at https://developers.google.com/earth-engine/datasets/catalog/WHRC_biomass_tropical. The two Jet Propulsion Laboratory AGB maps are available at https://gfw2-data.s3.amazonaws.com/forest_cover/zip/tropical_forest_carbon_stocks.tif.aux.zip and https://zenodo.org/records/7583611 (ref. 48), respectively. The atmospheric CGR data are available at https://gml.noaa.gov/. The links to the predictors for BRT models are provided in the Supplementary Information. The baseline map for map figures was obtained from GADM (https://gadm.org/). The generated maps of AGC losses and gains are available online at https://data.tpdc.ac.cn/zh-hans/data/97a05aa3-5d4f-4e44-99d1-e474166e62a6.
## Code availability

The scripts used to generate all the results are MATLAB (2022B). The code is available from the corresponding author on request.

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## Acknowledgements

This research has been funded by the European Space Agency Climate Change Initiative (ESA-CCI) Biomass project (ESA ESRIN/ 4000123662) and RECCAP2 project 1190 (ESA ESRIN/ 4000123002/18/I-NB). J.-P.W. acknowledges support from the CNES (Centre National d'Etudes Spatiales) TOSCA programme. P.C. and J.-P.W. acknowledge support from the One Forest Vision programme of the French Ministry of Research. This study was also supported by the CALIPSO (Carbon Losses in Plants, Soils and Ocean) funded through the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures programme.

## Author contributions

P.C., J.-P.W. and Y.F. designed the research. Y.F. performed the analysis. X.L. and J.-P.W. prepared the raw SMOS-IC L-VOD data. D.v.W prepared the fire emission product. Y.F. and P.C. wrote the draft. All authors contributed to the interpretation of the results and the writing of the paper.

## Competing interests

The authors declare no competing interests.

## Additional information

*Extended data* is available for this paper at https://doi.org/10.1038/ s41558-024-02115-x.

*Supplementary information* The online version contains supplementary material available at https://doi.org/10.1038/s41558- 024-02115-x.

*Correspondence and requests for materials* should be addressed to Yu Feng.

*Peer review information* *Nature Climate Change* thanks Roger Auch, Rico Fischer, Graciela Tejada and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

*Reprints and permissions information* is available at www.nature.com/reprints.
Extended Data Table 1 | Tropical aboveground biomass carbon loss from existing studies and this study

<table><thead><tr><th>Ecosystems</th><th>Study period</th><th>Loss processes</th><th>Loss (PgC yr<sup>-1</sup>)</th><th>References</th></tr></thead><tbody><tr><td>Forests</td><td>2003-2014</td><td>Deg. & def.</td><td>0.86</td><td>Baccini et al.<sup>9</sup></td></tr><tr><td>Forests</td><td>2000-2012</td><td>Def.</td><td>0.81</td><td>Harris et al.<sup>6</sup></td></tr><tr><td>Forests</td><td>2000-2010</td><td>Def.</td><td>0.88</td><td>Achard et al.<sup>60</sup></td></tr><tr><td>Forests</td><td>2002-2012</td><td>Def.</td><td>1.02</td><td>Tyukavina et al.<sup>61</sup></td></tr><tr><td>Forests</td><td>2001-2013</td><td>Def.</td><td>0.84</td><td>Zarin et al.<sup>23</sup></td></tr><tr><td>Forests</td><td>2001-2019</td><td>Def.</td><td>1.01</td><td>Feng et al.<sup>11</sup></td></tr><tr><td>Forests</td><td>2001-2020</td><td>Def.</td><td>1.45*</td><td>Harris et al.<sup>4</sup></td></tr><tr><td>Forests</td><td>2010-2020</td><td>Non-fire deg. & def.</td><td>0.68</td><td>This study</td></tr><tr><td>All ecosystems</td><td>2010-2020</td><td>Fires</td><td>1.11</td><td>This study</td></tr></tbody></table>

Deg. and def. refer to forest degradation and deforestation, respectively. The land area of the tropical domain varies in different studies. The study of ref. 61 has the largest study domain, while this study has the smallest due to the masking of invalid L-VOD observations and non-TMF regions. All the studies exclude tropical Australia.
<figure><img src="image_15.png" /><figcaption>**Extended Data Fig. 1** | Extent of the study region over the tropics.</figcaption></figure>

a, availability of valid L-VOD observations. b, availability of TMF products.

c, final study domain. The tropics are defined as the regions between 23.5°N and

23.5°S but excluding Australia, following our previous studies¹¹. The final study
domain covers the tropical regions covered by valid L-VOD observations and TMF
products.
<figure><img src="image_16.png" /></figure>

**Extended Data Fig. 2** | L-VOD-based AGC changes for each loss and gain process. AGC gross losses comprise three parts, including fire emissions, deforestation and forest degradation through non-fire means. Fire emissions, encompassing both forest and non-forest areas, were estimated utilizing the van Wees fire emission product²⁰. Non-fire deforestation and forest degradation were quantified using the Tropical Moist Forests (TMF) dataset. AGC residuals were

determined by subtracting AGC losses from AGC net changes. These residuals comprise AGC changes originating from old forests and non-fire shrub/savannah loss that are not detected by TMF, and vegetation regrowth/gain, with vegetation regrowth/gain emerging as the predominant part. Consequently, the AGC residuals were deemed a proxy for AGC gross gains.
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

Extended Data Fig. 3 | Changes in aboveground live biomass carbon (AGC) relative to 2010 from 2011–2020 over the tropics (a) and three tropical continents of America (b), Africa (c) and Asia (d). The solid lines indicate the mean of AGC changes and shaded areas represent ±1 s.d. among the 18 estimates (see Methods).
<figure><img src="image_21.png" /><figcaption>Extended Data Fig. 4 | Bivariate map of aboveground live biomass carbon (AGC) gains vs. the four most influential factors of FRP (a), $F_t$ (b), young forest regrowth (c), and $R_s$ (d) for spatial variability modeling. The unit for the y-axis of the legend (AGC gains) is MgC ha⁻¹yr⁻¹.</figcaption></figure>
<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

**Extended Data Fig. 5** | Continental partial dependence plots of the top four variables in explaining the spatial patterns and trends in aboveground live biomass carbon (AGC) gains. a–d, the top four variables in explaining the spatial patterns of AGC gains. The unit for the y-axis of AGC gains is MgC ha⁻¹ yr⁻¹.

e–h, the top four variables in explaining the trends in AGC gains. δ denotes the trends in time series. The unit for the y-axis of δAGC gains is MgC ha⁻¹ yr⁻¹. Dash lines are the 30 individual estimates and solid lines are the median (see Methods).
<figure><img src="image_30.png" /><figcaption>Extended Data Fig. 6 | Bivariate map of trends in aboveground biomass carbon (δAGC) gains vs. the four most influential factors of δSM (a), δR<sub>s</sub> (b), R<sub>s</sub> (c), and livestock density (d) for δAGC gains modeling. The unit for the y-axis of the legend (AGC gains trend, that is, δAGC) is MgC ha<sup>-1</sup> yr<sup>-2</sup>. δ denotes the trends in time series.</figcaption></figure>