# Global increase in biomass carbon stock dominated by growth of northern young forests over past decade

Hui Yang, Philippe Ciais, Frédéric Frappart, Xiaojun Li, Martin Brandt, Rasmus Fensholt, Lei Fan, Sassan Saatchi, Simon Besnard, Zhu Deng, et al.

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## To cite this version:

Hui Yang, Philippe Ciais, Frédéric Frappart, Xiaojun Li, Martin Brandt, et al. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade. Nature Geoscience, 2023, 16 (10), pp.886-892. 10.1038/s41561-023-01274-4. hal-04234962

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# Global increase in biomass carbon stock dominated by growth of northern young forests over past decade

Received: 2 November 2022

Accepted: 21 August 2023

Published online: 2 October 2023

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Hui Yang<sup>1,2,∗</sup>, Philippe Ciais<sup>1</sup>, Frédéric Frappart<sup>1,3</sup>, Xiaojun Li<sup>3</sup>, Martin Brandt<sup>1,4</sup>, Rasmus Fensholt<sup>1,4</sup>, Lei Fan<sup>1,5</sup>, Sassan Saatchi<sup>6,7</sup>, Simon Besnard<sup>8</sup>, Zhu Deng<sup>1,9</sup>, Simon Bowring<sup>1</sup> & Jean-Pierre Wigneron<sup>3</sup>

Changes in terrestrial carbon storage under environmental and land-use changes remain a critical source of uncertainty in regional and global carbon budgets. We generated global maps of annual live vegetation biomass using L-band microwave vegetation optical depth. Globally, biomass carbon stocks increased from 2010 to 2019 at a rate of 0.50 ± 0.20 PgC yr⁻<fnref n="1" /> with a year-to-year variability, closely mirroring the observations of the global atmospheric CO₂ growth rate. The main contributors to the global carbon sink are boreal and temperate forests, while wet tropical forests are small carbon sources, from deforestation and agriculture-related disturbances. We found that the tropical deforested and degraded old-growth forests (>140 yr) are nearly carbon neutral whereas temperate and boreal young (<50 yr) and middle-aged (50–140 yr) forests are the largest sinks. By contrast, dynamic global vegetation models show that all old-growth forests are large sinks and largely ignore the impacts of deforestation and degradation on tropical biomass. Our findings highlight the importance of forest demography when predicting dynamics of future carbon sink under changing climate.

Increasing vegetation biomass plays a critical role in mitigating the increase of atmospheric CO₂ concentration<fnref n="1" />. The carbon balance of biomass results from gains due to plant growth and increased forest coverage and losses due to harvest, deforestation, degradation, back- ground tree mortality and natural disturbances²,³. Both carbon gains and losses are affected by forest demography, environmental condi- tions and anthropogenic activities, which express large spatial and temporal variability<fnref n="4" />⁻⁶. Monitoring biomass carbon stocks over time

is essential to better understand and predict the effects of ongoing and future climate change and direct human management impacts on eco- systems, a key issue for land-based climate change mitigation policies⁷.

Few approaches and data sources have been used to estimate global and regional terrestrial biomass carbon budgets⁵,⁶,⁸⁻¹¹, and dis- parity in their predictions gave little cause for consensus. National reports of carbon stock changes suggest a global land carbon sink much smaller than that derived from the global carbon budget<fnref n="1" />,⁷,¹⁰. This

<footnote n="1">¹Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France. ²Department for Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany. ³UMR ISPA, Université de Bordeaux, Villenave d'Ornon, France.</footnote>

<footnote n="4">⁴Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark. ⁵Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China. ⁶Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. ⁷Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA. ⁸Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, Helmholtz Center Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany. ⁹Department of Earth System Science, Tsinghua University, Beijing, China. ✉e-mail: huiyang@lsce.ipsl.fr</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1 | Spatial patterns of changes in total biomass carbon density and forest area fraction during 2010–2019.** a, Mean annual changes in total live biomass (ΔTB) carbon density. b, The areal fraction of forest loss. The scatter plot shows the total changes in ΔTB versus total forest loss fraction over the pixels with ΔTB < 0 for each of 43 land regions worldwide. c, The areal fraction of forest</figcaption></figure>

gains. The scatter plot shows the total changes in ΔTB versus total forest loss fraction over the pixels with ΔTB > 0 for 43 land regions. Forest area loss and gain fractions maps in **b** and **c** are aggregated at the same resolution as the L-VOD data. EAS, East Asia; NWN, Northwest North America; RAR, Russian Arctic.

is partly because inventories do not cover unmanaged lands and are constructed using sparse data and simplified approaches such as fixed forest growth rates<sup>11,12</sup>. Analogously, terrestrial carbon sink predictions from dynamic global vegetation models (DGVMs) are inconsistent with carbon flux from atmospheric inversions, even for hemispheric-scale carbon budgets. DGVMs predict that tropical and northern forests are equal contributors to the global land sink<sup>9</sup>, while inversion models attribute it to northern regions<sup>11</sup>. The main source of DGVM uncertainty is their incomplete representation of biogeochemical processes and forest management and demography, while uncertainty of atmospheric inversions is due to atmospheric transport simulation errors and sparse atmospheric data<sup>13</sup>. Alternatively, remote-sensing data of the land surface have been used to diagnose changes in terrestrial carbon stocks from a mix of high-frequency radar data and optical and height observations. Ref. 5 modelled carbon gains and losses using a carbon accounting approach initialized with a biomass map of the year 2000, and produced a map of biomass carbon sinks, while ref. 6 mapped annual biomass carbon changes using a machine-learning model trained on optical and high-frequency microwave Earth observation data.

Adding to these methods is low-frequency L-band (1.4 GHz) vegetation optical depth (L-VOD) data obtained from Soil Moisture and Ocean Salinity (SMOS) satellite passive microwave observations, which probe live woody vegetative biomass components owing to the higher penetration of low-frequency radiation within the canopy layer. The spatial sampling resolution of L-VOD (~25 km) integrates vertical structural and horizontal heterogeneity across landscapes, which facilitates the estimation of mean biomass carbon stocks with little saturation<sup>14</sup>, compared with optical and high-frequency microwave data<sup>15</sup>. Therefore, L-VOD has been used to monitor tropical vegetation carbon changes at regional scales<sup>16,17</sup>. Several limitations have prevented the widespread application of L-VOD over the entire globe: (1) signal disruption from radio frequency interference (RFI) from human activities, particularly in northern populated area<sup>18</sup>, and (2) sensitivity to vegetative water content (VWC) implying an aliasing effect of varying phenology and water stress on the inference of biomass change<sup>19</sup>.

In this Article, we addressed these challenges by designing a new filtering method to remove the effects of RFI and VWC from L-VOD time series (Supplementary Methods and Supplementary Text 1). Then we derived the spatial and temporal distributions of total live biomass
<table><caption>**Table 1 | The net total biomass changes (PgC yr⁻¹) from 2010 to 2019 globally and for five biomes (wet tropics, dry tropics, arid, temperate and boreal)**</caption><thead><tr><th></th><th>Global</th><th>Wet tropics</th><th>Dry tropics</th><th>Arid</th><th>Temperate</th><th>Boreal</th></tr></thead><tbody><tr><td>L-VOD (2010–2019)</td><td>0.50±0.20</td><td>-0.07±0.02</td><td>-0.01±0.007</td><td>-0.02±0.03</td><td>0.13±0.09</td><td>0.37±0.12</td></tr><tr><td>Xu et al. Ref. 6 (2010–2019)</td><td>0.56</td><td>0.05</td><td>+0.11</td><td>-0.11</td><td>+0.23</td><td>+0.17</td></tr><tr><td>Harris et al. Ref. 5 (2001–2019)</td><td>2.05±13.24</td><td>0.46±2.16</td><td></td><td>0.18±0.22</td><td>0.97±12.97</td><td>0.43±0.30</td></tr><tr><td>Liu et al. Ref. 21 (1993–2012)</td><td>0.01</td><td>-0.15</td><td>-0.17</td><td>0.15</td><td>-0.004</td><td>0.19</td></tr><tr><td>Pan et al. Ref. 2 (1990–2007)</td><td>1.10±0.80</td><td>-0.11±0.80</td><td></td><td>-</td><td>0.72±0.08</td><td>0.50±0.08</td></tr></tbody></table>

Comparison of regional biomass changes from this study with the recent published other products. For the estimations from L-VOD, Harris et al. and Pan et al., mean±s.d. is shown in the table.

carbon (the sum of above- and below-ground biomass) of terrestrial ecosystems from 2010 to 2019. Above-ground biomass (AGB) was first retrieved from L-VOD on the basis of empirical spatial relationships regressing L-VOD against reference AGB datasets. Total biomass was then derived from AGB using a global map of the ratio between above- and below-ground biomass. Such L-VOD-derived maps of annual biomass carbon change were used to assess regional carbon budgets, attribute carbon losses and gains to forest cover change induced by fires and land-use change and investigate how forest age controls large-scale terrestrial carbon storage using a global map of forest age. The L-VOD-derived distribution of biomass carbon sinks and sources was also compared with the DGVMs from TRENDY v8 project, atmospheric inversions (Extended Data Fig. 9) and the mentioned previous biomass change datasets.

## Regional total live biomass changes

Between 2010 and 2019, net L-VOD-derived live biomass carbon stocks increased at a rate of 0.50 ± 0.20 PgC yr⁻¹ (mean ± s.d.; s.d. includes the uncertainty of the spatial calibration against different reference biomass maps and the uncertainty of the ratio between above-ground and total biomass; Methods). Notably, the uncertainties related to the above-ground and total biomass ratio exhibit spatial heterogeneity, with relatively high values in areas characterized by low vegetation cover and low carbon stocks (Supplementary Fig. 10), resulting in a minor impact on the global value. The global pattern of carbon sinks and sources exhibited strong spatial heterogeneity and biome-scale differences (Fig. 1a). Boreal (+0.37 ± 0.12 PgC yr⁻¹) and temperate (+0.13 ± 0.09 PgC yr⁻¹) forests were the two largest contributors to the global biomass carbon sink over the past decade. By contrast, wet tropical forests experienced a small net carbon loss (−0.07 ± 0.02 PgC yr⁻¹), while arid biomes and dry tropical forests were carbon neutral (−0.02 ± 0.03 PgC yr⁻¹ and −0.01 ± 0.007 PgC yr⁻¹, respectively). The net biomass reduction in the wet tropical forests probably resulted from deforestation, degradation and moisture stress, in particular over southeastern Amazonia, as shown by a recent regional inversion study²⁰ (Extended Data Fig. 1). Yet most of the undisturbed wet tropical forests were carbon sinks, in agreement with inventory data (see comparison with plot measurements in Supplementary Text 3).

We compared the global and regional total biomass changes derived from L-VOD with other independent global biomass products in Table 1. Compared with the L-VOD results, we found that ref. 6 has a similar global annual net biomass carbon sink of +0.56 PgC yr⁻¹ over the same period, and the model of ref. 5 and the data synthesis of ref. 2 have a higher carbon sink. Ref. 21 indicated no change in global biomass carbon, but their biomass carbon gains and losses in some regions seem biologically implausible. These differences could be related to different time coverage of products and methodologies. Ref. 2 compiled forest inventory data (very sparse in the tropics) and used a global land-use-change bookkeeping model to account for losses caused by

deforestation and degradation. Ref. 5 used a Tier 1 model initialized by a static biomass map and driven by maps of harvest removals and land-use and land-cover change data to estimate biomass changes averaged over the past 20 years. Ref. 21 used high-frequency VOD data with a weak capacity to penetrate the canopy, reflecting mainly carbon dynamics of the top canopy layer. At the regional scale, our L-VOD product and these four products nevertheless agree on a large carbon sink in boreal and temperate forests (except for ref. 21). Ref. 21 and ref. 2 showed large biomass carbon sources in the wet tropical forests, consistent with L-VOD, while ref. 6 reported a net annual carbon sink for this biome (+0.16 PgC yr⁻¹).

The spatial distribution of live biomass losses from L-VOD (Fig. 1a) corresponds generally to global forest areal losses estimated by the Landsat-based Global Forest Change data²². Specifically, where L-VOD suggested hotspots of biomass losses in the eastern Amazon, the central Congo Basin and some southeast Asian rainforests (Fig. 1a), a cumulative forest area loss of 1.98 × 10⁷ ha was reported from the Landsat-derived forest area data (Fig. 1b). However, L-VOD suggested biomass gains not only in zones of increasing forest area, but also where forests have been stable (Fig. 1c). We calculated the total losses and gains of live biomass and forest area for each of the Intergovernmental Panel on Climate Change Sixth Assessment Report subregions (Supplementary Fig. 7). Spatially, the magnitude of biomass losses increases with the total area of forest loss in each 25 km L-VOD grid cell ($R^2$ = 0.58) and vice versa for forest gains ($R^2$ = 0.17). The relatively low $R^2$ for the relationship between biomass and forest area gains could be due to the mismatch of the three subregions (shown in Fig. 1c): East Asia, Russian Arctic and Northwest North America. This mismatch reflects limitations of both global forest area gain and L-VOD data. For example, Landsat imagery has limited capability to detect exact year of tree canopy closure and distinguish regrowing forest from plantations and extra-forest woody systems establishment such as woody encroachment²³. This is especially the case for tree plantation areas in southern China. Moreover, the forest area gain product is a binary indicator with either 1 (gain) or 0 (no gain) for the entire period. Due to a lack of annual forest gain data, we assumed that gains have taken place uniformly each year during the study period. However, biomass stock changes from L-VOD are affected by RFI in China and Middle East (Supplementary Fig. 3) and by the emissivity of inundated areas and wetlands in Russian Arctic and Northwest North America regions²⁴.

## Live biomass changes in relation to disturbances

To attribute the contribution of anthropogenic disturbances to changes in live biomass inferred from L-VOD during the past decade, we combined a global classification map of the drivers of forest loss (Extended Data Fig. 2) with our biomass change maps (Methods). We found that in both the wet and dry tropics, deforestation, defined as the long-term, permanent conversion of forest/shrub to non-forest, is the most important driver of biomass loss. The process of deforestation led to a net
<figure><img src="image_5.png" /><figcaption>**Fig. 2 |** *Changes in live biomass carbon density for global and five climatic biomes.*** **a–f.** ΔTB are estimated using L-VOD, ref. 6 and 15 TRENDY DGVMs. ΔTB were calculated for global (a) and five climatic biomes: wet tropics (b), dry tropics (c), arid (d), temperate (e) and boreal (f). The stacked bars of L-VOD and ref. 6 show the subcomponents of ΔTB that are caused by no forest loss (grey),</figcaption></figure>

deforestation (red), agriculture (yellow), forestry (green), wildfire (brown) and urbanization (dark blue), and the horizontal black and blue lines show the net changes of ΔTB. Horizontal lines in box plots show, from top to bottom, 95th, 75th, 50th, 25th and 5th percentiles.

biomass loss of 0.04 PgC yr⁻¹ in the wet tropics and 0.06 PgC yr⁻¹ in the dry tropics (Fig. 2b,c). According to L-VOD data, agricultural disturbances, defined as forest/shrub systems subsequently converted to agricultural use, constitute the second critical driver of global live biomass losses. Agricultural disturbances account for a biomass loss of 0.03 PgC yr⁻¹ in the wet tropics and 0.03 PgC yr⁻¹ in the dry tropics. These tropical biomass losses are smaller than land-use-change carbon emissions estimated from bookkeeping models, for example, ref. 25, because at the spatial sampling of L-VOD (-25 km), carbon sinks in intact and secondary forests co-exist with carbon losses from land-use change. In the dry tropics, the negative effects of deforestation and agriculture on live biomass are almost completely offset by the positive effects of forestry and vegetation growth and regrowth (+0.08 PgC yr⁻¹). Such biomass gains offset in dry forests may be associated with woody plant encroachment due to warmer and wetter climates as well as elevated CO₂ concentration²³,²⁶. By contrast, in the wet tropics, disturbance-induced losses dominate the net change of biomass (Fig. 2c).

In subtropical, temperate and boreal biomes, net forest area gains and forestry activities, defined as wood-fibre plantations and other forestry activities, including clearcutting and selective cuts, are associated with massive biomass gains in the L-VOD data. Total biomass gains over 'forestry'-classified areas sum up to +0.16 PgC yr⁻¹ in boreal forests and +0.12 PgC yr⁻¹ in temperate ones (Fig. 2e,f). These findings are consistent with regional studies showing that forest plantations and management led to a large forest sink in southern China²⁷, and rubber plantations in mountainous Southeast Asia (for example, Cambodia and Indonesia) resulted in net carbon sequestration²⁸.

However, wildfires are the dominant driver of boreal forest area loss, with areas that experienced fire disturbance cumulatively losing 0.02 PgC yr⁻¹ of live biomass. Such an estimate is smaller than fire carbon emissions because L-VOD data have a coarse resolution of 25 km and do not permit distinguishing carbon gains from unburned forests and/or regrowing forests within each L-VOD grid cell.

## Comparison with DGVMs

The TRENDY DGVMs show a quite uncertain net carbon neutral in the temperate region with a large spread between models (+0.03 ± 0.10 PgC yr⁻¹, ensemble mean ± s.d. across models) and a larger sink in boreal forests (+0.28 ± 0.16 PgC yr⁻¹) (Fig. 2). The boreal forest carbon sink of these models is consistent with L-VOD, but their temperate forest sink is less than half of the L-VOD-derived value, probably due to an incomplete representation of plantations and forestry in the Land Use Harmonization dataset used to drive all DGVMs (Extended Data Fig. 3). In the tropics, the TRENDY models predict live biomass changes of opposite sign to those of the L-VOD, that is, a carbon sink in the wet tropics (+0.07 ± 0.08 PgC yr⁻¹) and a large carbon source in the dry tropics (-0.11 ± 0.10 PgC yr⁻¹). The carbon gains in the wet tropics from DGVMs may result from the fact that they do not account for light limitation in dense forest stands; drought-induced mortality, which increases the turnover of biomass; emerging nutrients (phosphorus) limitations on growth²⁹; tropical forest degradation; and small fires³⁰,³¹.

Perhaps a more critical limitation of DGVMs regarding their prediction of forest carbon uptake is their lack of consideration of distinct carbon sequestration rates across forest age classes. To demonstrate this, we binned the L-VOD data into eight age classes according to a
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**Fig. 3 | Relationship between ΔTB and forest age, canopy height.** a, Bars indicate the mean value of ΔTB density during 2010–2019 partitioned to different classes of forest ages (20 yr intervals). Total ΔTB density in each class is derived from L-VOD (upper panel) and the mean of 15 TRENDY DGVMs (bottom panel). The total ΔTB density of intact forests is shown as white bars (the width of white bars represents the area of intact forests). The width of coloured bars represents the forest area of different forest classes. The individual models show

<figure><img src="image_7.png" /></figure>

agreement on the relationship between ΔTB and forest age (shown in Extended Data Fig. 5). **b**, ΔTB density partitioned to age categories of <50 yr, 50–140 yr and >140 yr, as well as for temperate and boreal (upper panel) and tropical and arid (bottom panel) biomes. The Köppen-Geiger climate classification map is used to determine biome types. The error bars of TRENDY in panels **a** and **b** denote the uncertainty (s.d. across 15 TRENDY DGVMs) of total ΔTB density in each forest class.

<figure><img src="image_8.png" /></figure>

**Fig. 4 | Comparison of the IAV in global carbon fluxes and global atmospheric CGR.** The IAV of reversed global atmospheric CGR (black line), global total carbon fluxes in all forests (solid lines) and young forests (dashed lines) from L-VOD without CWD buffering (red lines) and total carbon fluxes from L-VOD with CWD buffering (blue lines) from 2012 to 2019. The right-hand panel shows the contribution (%) of all forests in five climate biomes to the IAV in global carbon

fluxes from L-VOD without CWD buffering (red bars) and with CWD buffering (blue bars). Additionally, the contribution of young forests among all forests is represented by the white bars. The global carbon fluxes (δTB) are calculated by using annual changes in live total biomass compared to the previous year. The shadings of each curve show the uncertainty of CGR and the standard deviation of L-VOD TB estimated by different calibrations.

global forest age map from ref. 32 and found that young (<50 yr) and middle-aged (50–140 yr) temperate and boreal forests have the largest biomass carbon sink (Fig. 3a,b). Conversely, old-growth forests (age >140 yr), which represent approximately 10% of global forest area, showed a net biomass loss over the past decade (Fig. 3a). Old-growth forests losing biomass are located mainly in the tropics (Fig. 3b), where the loss is caused by disturbances and degradation, as found in regional inventories³³ and satellite studies³⁴,³⁵. However, intact old-growth forests in the temperate and boreal biomes acted as a biomass carbon sink of 0.40 ± 0.93 MgC ha⁻¹ yr⁻¹, consistent with data from 519 plots analysed by ref. 36 (the details of this comparison are given in Supplementary Text 4).

Note that the forest age map from ref. 32 has high accuracy in northern latitudes, which adds confidence to our finding that young and middle-aged forests in temperate and boreal biomes are the largest carbon sinks (per unit area). However, the age assigned to old-growth forests in tropical regions is uncertain and may be overestimated. Since age is correlated with height, we also used canopy height data derived from space-borne Lidar measurements³⁷ to examine the relationship between biomass change and height. Canopy height data may have uncertainties in boreal regions due to the scarcity of recent lidar measurements above 50° N. We found that the relationship between biomass changes and canopy height exhibits a resemblance to the one with forest age. That is, the areas with tall trees (>35 m) corresponding
to old-growth forests are a carbon source per unit area and a very small sink when excluding grid cells with degradation and deforestation (Extended Data Fig. 4).

The TRENDY DGVMs simulate biomass changes per unit area over young and old-growth forest bins that are opposite to those from L-VOD. The models predict the largest carbon sinks over old-growth forests and small sources over young forests (Fig. 3a and Extended Data Fig. 5). This is because they have a long residence time for living biomass in high-biomass ecosystems (thus a higher sink potential in response to, for example, rising CO₂) and fail to consider the differences in CO₂ and climate effects on biomass accumulation across forest age classes. As illustration of the potential improvement of DGVMs in this regard, one model study that did incorporate forest age into a DGVM (LPJ-GUESS) predicted carbon sinks in young forests³⁸, in line with the results derived from L-VOD.

## Annual variability of carbon fluxes in and out of live biomass

Finally, we calculated interannual variability (IAV) in total live biomass (δTB; Methods) and compared with the fluctuations of the global atmospheric CO₂ growth rate (CGR). The CGR anomalies include not only net biomass change but also soil CO₂ respiration and CO₂ emissions from the decay of dead trees³⁹. Furthermore, live biomass losses in a given year due to tree mortality events induce 'legacy' CO₂ emissions from decaying coarse woody debris (CWD) and litter pools, which are particularly important in the tropics⁴⁰. Therefore, we estimated CWD decomposition rates using forest inventory data from ref. 2 and calculated their net CO₂ losses using a simple first-order kinetics model (Methods). By adding the annual legacy CO₂ emissions from CWD decay and mineralization fed by annual biomass losses derived from L-VOD, we found that the IAV in carbon fluxes from live biomass and CWD pools more closely matched the CGR (root mean square error, RMSE = 1.31 PgC; Fig. 4). This is especially clear for carbon loss anomalies after large mortality events in 2012 and 2015. As shown in biomass change maps (Extended Data Fig. 6), large carbon loss anomalies in 2015 were in European and tropical forests that suffered from severe droughts⁴¹,⁴², while those in 2012 were in Northeast Brazil and Australia, which experienced extreme forest fires⁴³,⁴⁴, as well as in the western United States due to drought⁴⁵.

Finally, we estimated the IAV of forest live biomass and CWD carbon across biomes and age classes and determined their contribution to global variability. L-VOD estimates show that young forests (<50 yr) contribute only 21% to global carbon flux variability (Fig. 4). Consideration of legacy CWD emissions is important for understanding global variation in annual carbon fluxes. Thus, if CWD is not considered, the dry tropics alone would account for 44% of the IAV, whereas once included, the influence of the tropics on variability declines and is instead dominated by arid (41%) and temperate (30%) ecosystems (Fig. 4 and Extended Data Fig. 7).

Note that the role of VWC on VOD data has been raised, and its important contribution to the seasonal dynamics in L-VOD is well recognized⁴⁶. However, the influence of VWC when inferring long-term trends of vegetation biomass based on L-VOD remains inconclusive. Previous analysis of deriving long-term biomass changes from L-VOD, both with and without the removal of the VWC variations, produced comparable outcomes¹⁷. Nonetheless, in this study we sought to minimize the influence of VWC variations by fitting and subtracting the seasonal changes, as well as by using L-VOD data from the wettest periods (Supplementary Text 1, Extended Data Fig. 8), when VWC tends to be saturated and have less year-to-year variability. Moreover, we designed a strict filtering to remove the RFI-affected data, which brings down the available data in southern China and Middle East, especially during the early 2010s period, which may create uncertainty in the estimation of biomass decadal changes. In addition, the L-VOD could have higher uncertainties and carbon sink could be overestimated when the observation

footprint contains areas covered by peatlands. The carbon sink in the boreal biome decreases from 0.37 PgC yr⁻¹ to 0.32 PgC yr⁻¹ when screening out all the pixels with peatlands using a threshold of 10%.

L-VOD data from the microwave SMOS satellite provides direct space and time-continuous information on carbon dynamics in vegetation total live biomass, giving new insight to the driving forces behind variations in the global carbon budget, including region-specific environmental processes (for example, El Niño) and human land-use change. We found that temperate and boreal biomes have been the foci of terrestrial live biomass accumulation over the past decade, due mainly to forest growth and forestry activities, while deforestation and agriculture have caused a net live biomass loss in the wet and dry tropics, a result that DGVMs are unable to reproduce. We found that this could be due to the lack of forest age representation in DGVMs, such that the carbon sequestration capacity of old-growth forests is overestimated. Factoring in the lagged effect of coarse woody debris decay, we found that interannual variations of the global carbon flux derived from L-VOD are in agreement with in situ atmospheric CO₂ growth-rate observations. This information constitutes a crucial step towards an improved understanding and prediction of biomass responses to future climate change. Our satellite-based approach provides an independent and spatio-temporally explicit estimate of the global carbon balance, around which the climate mitigation goals of the Paris Agreement (UNFCCC, 2016) revolve.

## 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/s41561-023-01274-4.

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# Methods

## L-VOD data

L-VOD related to the carbon and water content within vegetation has been widely used as a proxy of vegetation biomass for monitoring changes in carbon stocks (for example, refs. 47,48). Compared with high-frequency VOD products (for example, C/X/K-VOD), low-frequency L-VOD data have been proved to be more sensitive to the woody component of the vegetation and do not saturate at high biomass because the low-frequency microwaves can penetrate deeper within the forest canopy layer¹⁴. The SMOS is the first L-band mission and provides global datasets with 1-to 3-day revisit times from ascending (ASC) and descending (DESC) orbits since 2010. In this study, L-VOD data without urban effects are derived from SMOS using the SMOS-IC algorithm⁴⁸. Data are made available by INRAE (Institut National de Recherche Agronomiques) Bordeaux Soil Moisture and VOD Products at https://ibremotene-sensing.inrae.fr/. As the L-VOD could be underestimated when the observation footprint contains substantial open water bodies²⁴, we have removed the 25 km pixels where the areal fraction of regularly flooded wetlands (including seasonal and permanent wetlands and permanent and intermittent rivers) or lakes exceeds 80% using global maps from ref. 49. Moreover, we performed tests using thresholds of 80%, 50% and 30% and have confirmed that the choice of thresholds has negligible impacts on the estimation of the global and regional carbon budget (Supplementary Fig. 11). In addition, in light of the concerns regarding the high uncertainty in L-VOD observations within the footprint encompassing peatlands, we identified the 25 km pixels where the peatland fractional coverage is more than 10%, on the basis of a map of peatland fractional coverage from ref. 50. Subsequently, we conducted a comparison of the global and biome-level total biomass changes considering with and without peatland grid cells. This allowed us to assess the impact of peatland areas on the estimation of biomass changes.

## New filtering method for L-VOD data

Previous studies applied an efficient but simple method to filter L-VOD data using the root mean square of the measured and simulated brightness temperature (referred to as RMSE-TB). The performance level of this simple filtering method has been confirmed in the pan-tropical regions¹⁶,¹⁷. However, for some Northern Hemisphere regions where L-VOD data are seriously disturbed by RFI, the old simple filtering method has limited capacity. Thus, we designed a new and strict filtering method for selecting the 'best'-quality data from both ascending and descending orbits. After the strict filtering, we fitted and reconstructed the L-VOD data using a curve fitting method from ref. 51, which is used to filter, smooth and reconstruct atmospheric CO₂ measurements by the National Oceanic and Atmospheric Administration. The details of the filtering, fitting and reconstruction processes and the evaluation of filtering method are given in Supplementary Texts 1 and 2.

## Total live biomass derived from L-VOD

Strong relationships have been found between VOD and AGB at regional to global scales, from C- to L-band products. All the existing AGB maps contain uncertainties and bias, and none can be considered as closer to the truth (true AGB map is unknown); we focused on utilizing (1) the spatial gradients of AGB derived from the existing maps (rather than the local AGB values) and (2) the temporal dynamics derived exclusively from L-VOD. Thus, in this study we build spatial calibration functions relating L-VOD to AGB with four existing tropical/global AGB maps (for example, refs. 52–55), at 25 km resolution (the original resolution of L-VOD). Instead of building a single global spatial calibration function like in previous studies where outliers can impact the fit parameters, we divided the world into three parts—Northern Hemisphere (30° N–90° N), tropics (30° S–30° N) and Southern Hemisphere (90° S–30° S)—and then found the best coefficients of two spatial calibration functions for the three parts of the world separately.

The choice of the best calibration function in our analysis was then based on the coefficient of determination (R²), which is shown in Supplementary Table 1 and Supplementary Fig. 8.

$$AGB = a_1 \frac{\arctan(b_1 (\text{VOD} - c_1)) - \arctan(b_1 (0 - c_1))}{\arctan(b_1 (\infty - c_1)) - \arctan(b_1 (0 - c_1))} + d_1$$

$$AGB = \frac{a_2}{1 + \exp(-b_2(\text{VOD} - c_2))} + d_2$$

where $a_1, b_1, c_1, d_1, a_2, b_2, c_2$ and $d_2$ are the fitted coefficients.

Nine sets of calibration parameters were used for the tropics (30° S–30° N), two sets of calibration parameters were used for the Northern Hemisphere (30° N–90° N), and two sets of calibration parameters were used for the Southern Hemisphere (90° S–30° S) (Supplementary Table 1). To estimate the total live biomass from AGB, we used the ratio of AGB to total live biomass over each 25 km pixel from ref. 56. The uncertainty of the total live biomass changes was calculated as follows. (1) We calculated the relative uncertainty of the first year's biomass and the last year's biomass separately, using the ratio of standard deviation and mean of the estimates from 18 sets of calibration parameters (that is, $\sigma_1/\mu_1$). (2) According to propagation of uncertainty, the uncertainty of calibration parameters was calculated as

$$\sigma_{\text{calibration}} = \Delta \text{TB} \sqrt{(\sigma_{2010}/\mu_{2010})^2 + (\sigma_{2019}/\mu_{2019})^2}$$

(3) To account for the uncertainty in our results associated with the AGB and total biomass ratios, we utilized a bootstrapping method to estimate the uncertainty of AGB/(AGB + BGB) (Here, AGB represents aboveground biomass while BGB represents belowground biomass. Supplementary Fig. 10). The separate uncertainty, values for AGB and BGB, as provided by ref. 56, were used to generate 500 random samples of AGB and BGB, assuming normal distributions. The uncertainties of the AGB/(AGB + BGB) ratios were subsequently computed as the interquartile range of these generated samples since the distribution of these AGB/(AGB + BGB) samples is a non-normal and left-skewed distribution. (4) The uncertainty of global and biome-level total biomass changes were calculated as

$$\sigma_{\Delta \text{TB}} = \sqrt{\sigma_{\text{calibration}}^2 + \frac{\sigma^2_{\text{AGB}}}{(\text{AGB} + \text{BGB})}}$$

Considering that seasonal changes in vegetation moisture content are not persistent in the long-term record, the long-term trends in L-VOD data reflect mainly biomass changes, while the seasonal effects of water stress on L-VOD are filtered out. Moreover, the annual values of live biomass are calculated using the monthly reconstructed long-term trend data from the wettest periods and as the averages of May, June, July and August (that is, July 1-centred averages) (Supplementary Text 1). This is because during the wettest periods, water content tends to be saturated and have less year-to-year variability. The net changes in total live biomass (ΔTB) are calculated as the difference in annual live biomass between 2010 and 2019. Because there is no seasonal cycle in the reconstructed long-term trend data, ΔTB estimated using different month-centred averages have no significant difference (Extended Data Fig. 8).

The SMOS observations from the ascending orbits were measured in the morning at 6:00, while those from the descending orbits were measured in the afternoon at 6:00. So the total biomass data derived from the ascending and descending orbits should be nearly identical because carbon is accumulated slowly in the vegetation elements. The ΔTB values were generally calculated using the ASC and DESC merged data, which combine all the filtered good-quality VOD data from ascending and descending orbits. If the L-VOD-derived live biomass data obtained from ascending and descending orbits have large differences, the most likely cause is that either ASC data or DESC data were disturbed by RFI and that this would be reflected in a smaller value than the actual value. In this case, ASC and DESC merged data may underestimate actual changes in biomass. Thus, we compared
the pixel annual ASC-only and DESC-only data for the first year of 2010 and the last year of 2019. If the difference between ASC-only and DESC-only data was greater than 10% of their mean, we used the higher of ASC-only and DESC-only data instead of ASC and DESC merged data. Moreover, the global carbon fluxes (δTB), which are the losses or gains in total live biomass within one year, were calculated using the reconstructed smoothed data. It is the difference in L-VOD-derived live biomass between year *n* and year *n* + 1.

## Comparison with the 'top-down' estimates

We also used 'co2flux' outputs of two inversion models: SURF and GOSAT. Two inversion models, SURF and GOSAT, provide CO₂ atmospheric inversions over 2010–2018 and 2010–2015, respectively⁵⁷. The assimilated observations for GOSAT models are satellite column retrievals. GOSAT is a Japanese satellite that was launched in January 2009. The assimilated observations for SURF are surface air-sample measurements. The transport model for both of them is LMDz, the atmospheric component of the Earth system model of the Institut Pierre-Simon Laplace. We adjusted the net biome productivity (NBP) from inversions using the CO₂ flux caused by lateral transport processes from rivers and crop and wood carbon productions, estimated by ref. 12. As shown in Extended Data Fig. 9, both SURF and GOSAT inversions show that the wet tropics are a CO₂ source, and the temperate and boreal regions are a CO₂ sink, consistent with our L-VOD estimates. However, the L-VOD product measures only carbon changes in live vegetation, whereas inversions reflect carbon dynamics over the whole terrestrial system to include soil carbon, litter and dead wood, perhaps explaining regional differences between the two approaches.

We also noted that our net biomass carbon sink is lower than the global residual terrestrial net anthropogenic land CO₂ sink of 1.9 PgC yr⁻¹ estimated by the Global Carbon Project (GCP)¹. However, for reconciling these two estimates, lateral land-to-ocean carbon export fluxes, and crop and wood products horizontal carbon displacement should be removed from the GCP estimates³. According to the analysis by ref. 12, the riverine export of terrestrial carbon to the ocean totals ~0.99 PgC yr⁻¹, while the crop and wood products displacement is about ~0.01 PgC yr⁻¹ (Supplementary Table 2), which altogether reduces the GCP estimate of the anthropogenic land CO₂ sink to ~0.92 PgC yr⁻¹, which remains substantially 0.51 PgC yr⁻¹ higher than the L-VOD biomass sink. One explanation for this discrepancy is that the GCP corrected value still includes a soil organic carbon sink, which is not accounted for in L-VOD estimates. As an observation-based global estimate is still lacking, we used the soil organic carbon sink from TRENDY DGVMs, which provided a range of ~0.1 to 3.3 PgC yr⁻¹. On the basis of these modelled estimates, we removed the soil organic carbon sink in the GCP estimate of the anthropogenic land CO₂ sink and found that the GCP estimate of biomass-only sink matches with our L-VOD biomass sink.

## Forest gain and loss data

We used the global forest change product of ref. 22 to calculate forest loss and gain fractions over the period 2010–2019. In the global forest change product, forest loss is defined as a stand-replacement disturbance, or a forest to non-forest change (due to natural and anthropogenic disturbances); forest gain is defined as a non-forest to forest change. The loss product is annual, while the gain product is one value, either 1 (gain) or 0 (no gain) for the entire period of 2000–2019. Forest pixels were defined using the Hansen tree-cover map²², depending whether tree cover was above 30%. Then all the 30 m forest pixels were classified into one of two categories: (1) no forest loss or (2) forest loss during 2010–2019. Then for each 0.25 degree pixel, we calculated the percentage of the 30 m pixels with forest loss, which is the loss in forest cover fraction in Fig. 1b. Similarly, all the 30 m forest pixels were classified into one of two categories: (1) no forest gain or (2) forest gain during 2010–2019. The percentages of the 30 m pixels with forest gain

were calculated in each 0.25 degree pixel (Fig. 1c). Note that the global forest change product of ref. 22 is based on satellite data that exclude information on changes in forest density beyond a certain threshold. This means that sharp changes in the landscape from year to year, such as clearcuts and large natural disturbances, can be well identified, whereas incremental changes cannot be detected⁵⁸.

## Disturbance map

The forest disturbance classification map was generated by ref. 59 at 10 × 10 km spatial resolution. This map was first aggregated to the areal fractions of each disturbance type at 25 × 25 km grid cell (Extended Data Fig. 2) and the total live biomass changes due to five disturbance drivers (deforestation, agriculture, forestry, wildfire and urbanization) described by the net change of biomass over the pixels affected by each disturbance.

Our simplified attribution of biomass loss to different drivers used the classification established from the interpretation of high-resolution sample images. In reality, different factors, such as deforestation, fires, agriculture and forestry, may coincidentally or sequentially occur at the same location. For example, in northern Siberia forests, wildfires may take place in previously logged areas, or logging activity may occur after a fire event. In Southeast Asia, 'forestry' in ref. 59 could also be categorized into 'deforestation'. Therefore, the simplified classification and the lack of high-resolution information about biomass density before forest loss cause uncertainty in our attribution of biomass losses to drivers. Moreover, the different effects of disturbances on below-ground biomass have not been taken into account; for example, the burning of fires may destroy and release only the above-ground component, and most of the below-ground component can stay in the soil. To achieve a detailed investigation, a high-resolution biomass change map that can clearly distinguish the burned and regrowing biomass is needed.

## Outputs of DGVMs

We used 'cVeg' outputs (total live biomass, the sum of above- and below-ground biomass) of 15 DGVMs from the TRENDY v8 project¹: CABLE-POP, CLASS-CTEM, CLMS.0, DLEM, ISAM, ISBA-CTRIP, JSBACH, JULES-ES, LPJ-GUESS, LPX-Bern, OCN, ORCHIDEE, ORCHIDEE-CNP, SDGVM and VISIT. TRENDY DGVMs performed three factorial simulations, and we used S3 simulations, which were driven with the same time-varying climate, CO₂ and land-use forcing data. The climate forcing data are from the CRU-JRA55 reanalysis dataset, which includes precipitation, snowfall, temperature, short-wave and long-wave radiation, specific humidity, air pressure and wind speed at 6 h temporal resolution. CRUJRA55 dataset is based on the JRA-55 (Japenese 55-year Reanalysis) dataset and aligned with the CRU (Climatic Research Unit) TS dataset. The global atmospheric CO₂ forcing data were derived from ice-core CO₂ data merged with National Oceanic and Atmospheric Administration annual data from 1958 onwards. Land-use-change forcing data were derived from the LUH2v2h reconstruction⁶⁰. The LUH2v2h data originates from the Land-Use Harmonization project version 2, serving as a global forcing dataset that provides annual land cover information.

## Forest age and canopy height data

The global forest age map was generated by ref. 32 using a machine-learning approach. The original gridded forest age data are at a spatial resolution of 1 km of several age classes (0–150+ with a decadal interval). We then calculated the area-weighted 1 km forest age data for each 0.25° pixel. Global canopy height data at a spatial resolution of 0.5° was generated by ref. 37 using the waveform data from Global Ecosystem Dynamics Investigation (GEDI) by a supervised machine-learning approach.

## The effects of coarse woody debris decomposition

Dead trees and branches, which form CWD, decay slowly and thus delay CO₂ emissions to the atmosphere after mortality events, particularly in
tropical forests. In this study, we considered the lagged effects of CWD decay following mortality on carbon fluxes (δTB). First, we estimated the mortality-induced carbon loss (CWD stock) using annual live biomass changes and annual net primary productivity data from MODIS (Moderate Resolution Imaging Spectroradiometer; MOD17A3HGF). Following ref. 40, we adopted a simple first-order kinetics model to simulate carbon emissions from the decomposition of CWD from dead trees at the scale of continental South American, African and Southeast Asian tropical forests. The CWD decomposition rates *k*, which are the ratio of CWD production to CWD stock, were calculated using forest inventory data from ref. 2. The CWD production was calculated using the mean total living forest biomass and a mortality rate of 0.02 yr⁻¹. The decomposed CWD component during year *t* was calculated using a constant decomposition rate *k* and CWD stock. The non-decomposed CWD component during year *t*, that is (1 − *k*) × CWD(*t*), remains in the CWD pool to be decomposed in subsequent years.

**Data availability**

The L-VOD data from this study can be accessed freely through the SMOS-IC website ([https://ibremot-sensing.inrae.fr/](https://ibremot-sensing.inrae.fr/)). The global forest change product can be accessed from [https://earthenginepartners appspot.com/science-2013-global-forest/download_v1.7.html](https://appspot.com/science-2013-global-forest/download_v1.7.html). The forest disturbance classification map is freely available from [https:// www.science.org/doi/10.1126/science.aau3445](https://www.science.org/doi/10.1126/science.aau3445). The forest age datasets are freely available at [https://doi.org/10.17871/ForestAgeBGI.2021](https://doi.org/10.17871/ForestAgeBGI.2021). The simulations from TRENDY DGVMs are available at [https://sites exeter.ac.uk/trendy](https://sites.exeter.ac.uk/trendy).

**Code availability**

All computer codes for the analysis of the data are available from the
corresponding author on reasonable 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). H.Y. was supported by the CCI Biomass project of the Climate Change Initiative (ESA ESRIN/4000123662) funded by ESA and the Project Office BIOMASS (grant number 50EE1904) funded by the German Federal Ministry of Economics and Technology. J.-P.W. acknowledges support from the CNES (Centre National d'Etudes Spatiales) TOSCA programme. L.F. acknowledges support from the National Natural Science Foundation of China (Grant No. 42171339).

**Author contributions**

P.C., J-P.W. and H.Y. designed the research. H.Y. performed all of the analyses. F.F., X.L. and J-P.W. prepared the raw SMOS-IC L-VOD data. S. Besnard prepared the forest age product. Z.D. prepared the national inventory data. H.Y., J.-P.W., P.C., M.B., R.F., S.S., L.F. and S. Bowring interpreted the results and provided comments on the discussion. The manuscript was drafted by H.Y., J.-P.W. and P.C. with contributions by all co-authors.

**Competing interests**

The authors declare no competing interests.

**Additional information**

**Extended data** is available for this paper at
[https://doi.org/10.1038/s41561-023-01274-4](https://doi.org/10.1038/s41561-023-01274-4).

**Supplementary information** The online version contains supplementary material available at [https://doi.org/10.1038/s41561-023-01274-4](https://doi.org/10.1038/s41561-023-01274-4).

**Correspondence and requests for materials** should be addressed to Hui Yang.

**Peer review information** *Nature Geoscience* thanks

David Chaparro 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.

**Reprints and permissions information** is available at
[www.nature.com/reprints](https://www.nature.com/reprints).
<figure><img src="image_9.png" /><figcaption>(a)</figcaption></figure>

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**Extended Data Fig. 1** | Comparison with the results of an Amazon inversion study. (a) The right-hand side panel shows the air samples collected at the four sites influenced by the regions (light blue lines). The left-hand side panel is a comparison of the average of total carbon flux for four sites from Gatti et al. (ref. 20) and the average of total biomass changes from L-VOD for the influence regions of four sites. Error-bars of carbon flux are uncertainties related to the background and travel time trajectories for each sample used to compose

first the monthly mean and later the annual means, while error-bars of ΔTB are the standard deviation of total biomass changes from L-VOD for the influence regions. (b) L-VOD derived mean annual changes in total live biomass carbon density over 2010-2019. (c) Mean annual changes in total live biomass carbon density over 2010-2019 (data from ref. 6). (d) Total forest gross loss fraction over 2010-2019 using the Landsat-based Global Forest Change Data. Panel a reproduced with permission from ref. 20, Springer Nature Limited.
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**Extended Data Fig. 2** | The fraction of zero/minor loss, deforestation, agriculture, forestry, wildfire, and urbanization. The fraction of zero/minor loss (a), deforestation (b), agriculture (c), forestry (d), wildfire (e), and urbanization (f) with a spatial resolution of 25×25 km, aggregated from the forest disturbance map by Curtis et al. (ref. 59).
Forest fraction changes (2010-2018)

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**Extended Data Fig. 3** | Changes in forest fraction from 2010 to 2018 from the TRENDY model ensembles. The fraction of forest for each year was calculated using plant functional type fractions from each DGVM.
<figure><img src="image_21.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>Extended Data Fig. 4 | Changes in ΔTB density from L-VOD and from TRENDY DGVMs partitioned to different classes of canopy height (2 m intervals).</figcaption></figure>

The averaged changes in total live biomass (ΔTB) density from L-VOD (a) and from TRENDY DGVMs (b) during 2010-2019 partitioned to different classes of

forest ages (20-year intervals). The width of colored bars represents the forest area of different forest classes. The total ΔTB density of intact forests is shown as white bars (the width of white bars represents the area of intact forests) in panel a.
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**Extended Data Fig. 5** | Relationship between total live biomass changes from each TRENDY DGVM and forest age. The averaged changes in total live biomass (ΔTB) density during 2010-2019 partitioned to different classes of forest ages (20-year intervals). Total ΔTB density in each class area TRENDY DGVMs. The width of colored bars represents the forest area of different forest classes.
<figure><img src="image_38.png" /><figcaption>**Extended Data Fig. 6** | Global patterns of carbon fluxes for the year of 2012 and 2015. The carbon fluxes, which are the loss or gains in total live biomass within one year, are calculated using the difference in L-VOD derived live biomass between year *n* and year *n* + 1.</figcaption></figure>
<figure><img src="image_39.png" /><figcaption>(b) w/ CWD buffering</figcaption></figure>

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**Extended Data Fig. 7** | Interannual variability of regional carbon fluxes from L-VOD without and with coarse woody debris (CWD) buffering over 2012–2019. The regional carbon fluxes were calculated for five biomes, that

is, wet tropics, dry tropics, arid, temperate and boreal. Panel a displays the interannual variability without CWD buffering, while panel b presents the interannual variability with CWD buffering.
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**Extended Data Fig. 8** | Mean annual changes in above-ground biomass carbon density over 2010-2019. (a) The annual values of live biomass were calculated as the averages of November, December, January and February (that is, Jan 1-centered averages). (b) The annual values of live biomass were calculated

as the averages of May, June, July, and August (that is, July 1-centered averages).

(c) Difference in AGB change between Jan 1-centered averages and July 1-centered averages.
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**Extended Data Fig. 9** | Mean net biome productivity for global and five climatic biomes. Mean values of annual net biome productivity (NBP) are estimated using two inversion models (SURF, and GOSAT), and pink bars presents the adjusted NBP estimates using the river-to-estuary carbon flux and crop & wood carbon productions from Deng et al. (2021) (ref. 12 see Supplementary

Table S3). NBP were calculated for global (a), and five climatic biomes: wet tropics (b), dry tropics (c), arid (d), temperate (e), and boreal (f). The error-bars denote the standard deviation of annual NBP over the periods (2010-2018 for SURF and 2010-2015 for GOSAT).