# Exploring the application of Earth Observation datasets for SEEA carbon accounting and its comparison with national GHG reporting to the UNFCCC

Arnan Araza, Lars Hein, Yu Feng, Joana Melo, Martin Herold

## ► To cite this version:

Arnan Araza, Lars Hein, Yu Feng, Joana Melo, Martin Herold. Exploring the application of Earth Observation datasets for SEEA carbon accounting and its comparison with national GHG reporting to the UNFCCC. Science of the Total Environment, 2026, 1015, pp.181189. 10.1016/j.scitotenv.2025.181189. hal-05495637

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Research Paper

# Exploring the application of Earth Observation datasets for SEEA carbon accounting and its comparison with national GHG reporting to the UNFCCC

Arnan Araza a, e ID, *, Lars Hein a, Yu Feng b, c, Joana Melo d ID, Martin Herold e ID

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## GRAPHICAL ABSTRACT

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## ARTICLE INFO

Editor: J. Gan

## ABSTRACT

Global biomass and carbon datasets derived from Earth Observation (EO) are rapidly increasing. Here we assess how these datasets can be used to compile carbon accounts aligned with the Systems of Environmental Economic Accounting (SEEA). Five carbon pools are considered: above-ground, below-ground, deadwood, litter and soil organic carbon (SOC) using EO datasets that include CCI Biomass and CCI Land Cover – both

<footnote>a Earth Systems and Global Change, Wageningen University and Research, The Netherlands</footnote>

<footnote>b Laboratoire des Sciences du Climat et de l'Environnement (LSCE), France</footnote>

<footnote>c Eastern Institute of Technology, Ningbo, China</footnote>

<footnote>d European Commission, Joint Research Centre (JRC), Ispra, Italy</footnote>

<footnote>e Remote Sensing and Geoinformatics, German Research Centre for Geosciences, Helmholtz Centre Potsdam, Germany</footnote>

<footnote>* Corresponding author at: Earth Systems and Global Change, Wageningen University and Research, The Netherlands. E-mail address: arnan.araza@wur.nl (A. Araza).</footnote>
Dataset link: https://github.com/arnanaraza/cs
hiny

Keywords:

Environmental-economic accounting
Carbon Accounting
SEEA
UNFCCC
Earth Observation
CCI Biomass

subjected to independent map validation. We compiled carbon accounts across multiple accounting periods, from annual to decadal intervals, spanning 2010–2021 for six countries: Brazil, Mozambique, the Netherlands, the Philippines, Sweden and USA. Next, we compared the SEEA-aligned EO-derived carbon accounts with the national greenhouse gas (GHG) inventories submitted to United Nations Framework Convention on Climate Change (UNFCCC) after considering the areas covered by managed forests and emissions from forest degradation for better comparability of the two frameworks. The compiled SEEA carbon accounts indicate that the above-ground component is the dominant carbon pool in Brazil while SOC outweighs other carbon pools in Netherlands, Sweden, Philippines and Mozambique. Results show substantial inter-annual variation in carbon fluxes, exhibiting a strong inverse relationship with accounting period (|r| = 0.57–0.85). Such variability is notably higher than the values reported by countries to the UNFCCC. While UNFCCC and SEEA fluxes show moderate overall agreement (r = 0.47, 58% agreement whether fluxes are sequestration or emission), there were differences across countries and flux categories. Our compiled accounts showed lower SOC emissions than UNFCCC reports potentially underestimating peatland emission; and minimal carbon emissions from forest degradation. Other sources of disagreement could be influenced by country definitions of managed forests which can be inconsistent with EO-based forest management maps, and the dependency of countries on national forest inventories which are rarely updated annually. The findings and outputs from this study echo the potential and flexibility of EO datasets for carbon accounting and inter-comparison exercises.

# 1. Introduction

The rise in greenhouse gas (GHG) emissions and the increasing impacts of climate change underscore the urgent need for a comprehensive, reliable and accurate carbon accounting. The United Nations (UN) System of Environmental-Economic Accounting (SEEA) is an international statistical standard that provides an explicit framework for this purpose. The SEEA consists of two parts, the Central Framework and Ecosystem Accounting (UN, 2021). In the Central Framework, carbon emissions are included through the air emission accounts but in most countries, these accounts do not comprehensively cover emissions and sequestrations from Land Use, Land-Use Change and Forestry (LULUCF) sector. The SEEA Ecosystem Accounts (SEEA-EA) cover carbon sequestration as well as carbon retention (stocks) in all ecosystems and do not make a distinction between managed and unmanaged ecosystems.

Earth Observation (EO) involves the application of satellite data and remote sensing technology to monitor the planet's surface including forest ecosystems and their carbon stocks at the global scale. In recent times, there has been an increasing number of datasets to monitor the above-ground carbon pool, particularly above-ground biomass (AGB) and among these maps is the Climate Change Initiative (CCI) Biomass dataset produced using radar remote sensing. Radar sensors are suitable for forest monitoring, being able to operate night and day without cloud interference and are known to exhibit sensitivity to different forest structures. The CCI Biomass maps particularly the version 5 product include 2010 and 2015–2021 epochs (Santoro, 2024). With the availability of such multiple epochs, one can already populate carbon accounts at multiple accounting periods, including both stocks and flows. Carbon flows in this context is defined as the transfer of carbon from one component to another i.e., between forest ecosystem and atmosphere. Moreover, the CCI dataset comes at high resolution, providing carbon information at a hectare scale. High-resolution maps are suitable inputs when implementing a spatially explicit accounting framework like SEEA due to higher chances of detecting small-scale ecosystem extent changes and even carbon flows such as from forest degradation (Asner et al., 2010).

The above-ground carbon stock is commonly used to derive the below-ground carbon whenever root-to-shoot ratio (root:shoot) information is available. The root:shoot ratio can be spatially defined as a function of climatic and environmental variables, delineating regions with distinct root:shoot characteristics. This regional root:shoot paved the way to produce below-ground biomass (BGB) maps at global scale e.g., the Spawn et al. (2020) dataset. Instead of using regional root:shoot information, Huang et al. (2021) used actual root harvest data from field measurements across the globe as calibration data to map BGB at 1 km spatial scale. They used the GlobBiomass dataset of Santoro et al. (2021) to calibrate the model producing the BGB map,

which was based on an earlier radar-derived AGB map from the CCI dataset. Aside from the below-ground component, other carbon pools such as deadwood and litter can be estimated from AGB as a function of the climate and environmental variables. For instance, Harris et al. (2021) applied the ratios recommended in the Intergovernmental Panel on Climate Change (IPCC) 2019 Guidelines to derive deadwood and litter maps globally. On the other hand, independent to the AGB is the Soil Organic Carbon (SOC). There is also a list of SOC datasets from multiple EO data sources and most of them originated from the maps of International Soil Reference and Information Centre (ISRIC). The ISRIC dataset has undergone a series of improvements including the increase in spatial resolution from 1 km to 250 m (Hengl et al., 2017), and the publication of an associated uncertainty layer (Poggio et al., 2021). Feng et al. (2022) used the higher resolution SOC (250 m) as a baseline to estimate the 2000–2020 SOC changes as a function of land use change and forest type at the pantropical scale.

The variety of potential datasets from EO comes with both advantages and disadvantages. The obvious advantage is the extensive data availability and options. However, like any other map derived from EO data, there are inherent risks from map uncertainties. To better understand and mitigate these uncertainties, it is essential to follow standard protocols in using EO maps especially conducting map validation (Olofsson et al., 2020). Map validation should be conducted using entirely independent reference data that have not been utilized in the calibration of the EO products. The process also allows estimation of uncertainties at aggregated levels used for subsequent biomass map applications e.g., REDD+ monitoring and carbon potential estimation (Yanai et al., 2020). Moreover, map validation can serve as the basis for assessing the accuracy of carbon accounts.

Once the SEEA carbon accounts are compiled, accounts can be compared to those derived from similar measurement frameworks (Keith et al., 2019). Such comparison of carbon accounts with GHG inventories at the country level is still lacking. The main counterpart of the SEEA framework concerning carbon accounting is the United Nations Framework Convention on Climate Change (UNFCCC) framework, which measures carbon flows and changes in carbon stocks. The UNFCCC framework is designed to report flows limited to direct anthropogenic disturbances (Keith et al., 2019). This implies that emissions related to forest fires without human interference and those resulting from ecosystem damages, for instance, due to typhoons are excluded. Moreover, the UNFCCC reporting is confined to managed forests. Recently, two publications have provided useful datasets that can make the accounts from the SEEA and UNFCCC frameworks more comparable. First, thanks to the compilation of Grassi et al. (2022) and colleagues, accessing the UNFCCC reporting for all countries has become efficient. The compilation also includes interpolated carbon stocks for countries with missing yearly reports. Second, the Lesiv et al. (2022) high-resolution (30 m) forest management dataset can
be utilized to differentiate between managed and non-managed forests, allowing comparison of national GHG inventories and SEEA-EA carbon accounts. Lastly, since the UNFCCC reporting includes emissions from harvest and forest degradation, the SEEA accounts need to categorize emissions due to deforestation and forest degradation. To achieve this, the ecosystem extents used for the accounts must be dynamic, requiring annually updated ecosystem extent maps.

Another important consideration when utilizing EO data for carbon accounting which remains underexplored in the literature is the analysis across varying accounting periods. Longer accounting periods may help mitigate uncertainties arising from the inter-annual variability of carbon stocks and fluxes (Santoro, 2024). Furthermore, averaging over a several years (e.g., 2009–2011 for opening stocks in 2010) has been identified as a practical mitigation strategy to address such variability (Araza et al., 2023). Exploring different accounting periods, such as annual, 5-year and decadal accounting periods can enhance the comparability of carbon accounts across frameworks. For instance, the UNFCCC relies on National Forest Inventories (NFIs), which are seldom updated annually while the Forest Resource Assessment (FRA) being released every five years.

Building upon our previous carbon accounting study in countries with available reference data in Araza et al. (2023), this study expands from accounting the above-ground pool to all carbon pools mainly using the CCI Biomass dataset that allows compiling accounts over multiple accounting periods. Improvements in spatial and temporal resolutions are applied to the Huang et al. (2021) BGB dataset and Feng et al. (2022) SOC dataset, respectively. The former is resampled from 1 km to 100 m spatial scale while matching the seven epochs of the CCI dataset, while the latter is expanded from pantropical to global scale while producing an annual SOC dataset instead of the published SOC limited to a single period. Furthermore, we compile accounts for both stocks and flows while comparing the latter with the flows reported to the UNFCCC framework. We also distinguish between managed and unmanaged lands in the accounts also using a forest management EO product. The following are the research objectives: (1) Conduct an independent validation for the EO-based inputs of the carbon accounts, (2) Compile the carbon accounts from EO data for all carbon pools (3) Compare the compiled accounts in this study with the country GHG reports to the UNFCCC, and (4) Assess the inter-annual variability associated with the accounting periods intervals.

## 2. Materials and methods

### Case countries and reference data

Earth Observation (EO)-derived national carbon accounting is demonstrated in six countries namely Brazil (BRA), Mozambique (MOZ), the Netherlands (NLD), the Philippines (PHL), Sweden (SWE) and the Contiguous United States of America (USA). The primary criteria for selecting these countries were the availability of reference data for map validation and their coverage of major climatic zones and continents. The reference data for BRA and USA are local above-ground biomass (AGB) maps from airborne light detection and ranging (LiDAR) missions while the rest are National Forest Inventories (NFIs). Refer to Fig. 1 for geographical illustrations of the study areas and locations where reference data samples were collected. The findings and outlooks from our previous study were also the reasons to implement this follow-up study in the same case countries.

Shown in Fig. 2 is the diagram of the datasets used and how they were combined to produce the carbon pools and eventually the carbon accounts. The steps are described in the upcoming sections.

### The earth observation carbon datasets

#### CCI Biomass dataset

We used the version 5 of the Climate Change Initiative (CCI) dataset which includes 2010, 2015–2021 maps at 100 m pixel size. While each map was derived from synthetic aperture radar (SAR) data where the signal is in the form of backscatter intensity, the version 5 maps used a collection of single-date images instead of annual mosaics (as previous CCI versions) from the radar data source (ALOS2-PALSAR2). The SAR data are incorporated into a model that considers the separate effects of the forest canopy and the ground on the returned signal (Santoro et al., 2022). The model is enhanced by allometric equations, which link canopy density to forest height, and subsequently to AGB (Santoro, 2020). These equations are based on the GlobBiomass dataset’s AGB (Santoro et al., 2021) and measurements of canopy density and height from spaceborne LiDAR. The CCI Biomass maps were subjected to the map assessment framework as correspondence to independent map validation protocol (Santoro, 2024). See Fig. S1 for the independent validation results of the dataset.

#### The global root biomass dataset

We used the Huang et al. (2021) dataset which used in-situ root harvest data from field measurements across the globe as calibration data to map below-ground biomass (BGB) at 1 km spatial scale. At this resolution, the approach accounts for both fine- and coarse-root biomass components in the root:shoot allometry. To avoid generalized root:shoot assumptions and potential overestimation of root biomass, as in other global datasets, comparisons were based on coarse root:shoot information at the level of forest classes and ecological zones. Another advantage of using the Huang et al. (2021) dataset is the consistency with the AGB dataset using a radar-based map where the CCI dataset is also built upon (Santoro et al., 2021). This shoot dataset was used together with the mapped root biomass to derive a global root:shoot map. This allowed to derive BGB maps of any epochs similar to the AGB maps.

#### The dynamic SOC dataset

Among the available soil organic carbon (SOC) datasets, we considered the SOC dataset of Feng et al. (2022) because its underlying model can be adapted to generate global annual SOC flux estimates. This dataset was originally developed for a pantropical forest carbon emissions assessment (2000–2021) and used ISRIC SoilGrids (baseline year 2000) combined with annual forest loss data from Hansen et al. (2013). The SOC fluxes were derived by applying land use change (LUC) specific emission factors synthesized by Don et al. (2010), which report absolute and relative SOC changes (Mg ha⁻¹, %) following major forest conversion pathways. For instance, conversion of primary forest to cropland results in a mean SOC decline of -20.1 ± 5.2 Mg ha⁻¹ (-25.2%), while conversion to perennial crops shows an even stronger decline of -32.0±3.5 Mg ha⁻¹ (-30.3%). In contrast, transitions such as cropland to secondary forest or fallow lead over time to SOC recovery (+33.2 ± 10.5 Mg ha⁻¹, +50.3%). These rates, which account for both immediate and gradual changes (5–20 years, on average), were applied across all mapped forest loss pixels. To expand beyond the pantropics, we extrapolated the same SOC response functions to non-tropical regions, consistent with the IPCC 2019 Guidelines that recommend biome or LUC-specific Tier 1 default factors when region-specific data are unavailable (Buendia et al., 2019). This procedure enabled the globally gridded annual SOC flux estimates that reflect both the magnitude and temporal trajectory of SOC changes following deforestation and subsequent land use transitions.
<figure><img src="image_5.png" /><figcaption>Fig. 1. Case countries and the reference data samples used to validate the CCI map.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 2. Datasets used for the carbon pools compilation and comparison.</figcaption></figure>

## Deriving the five carbon pool rasters

The above-ground carbon (AGC) was derived from AGB using the global multiplier 0.47 (Buendia et al., 2019; Harris et al., 2021). Using the AGC layers, the below-ground carbon (BGC) was derived using the root:shoot map from Yu et al. (2022) only after resampling such into 100 m to match the CCI map resolution. Similarly, the deadwood and litter were derived as fractions of the AGC and as a function of forest type, major climatic zones and precipitation datasets as recommended in the Intergovernmental Panel on Climate Change (IPCC) 2019 report (Buendia et al., 2019). The annual SOC maps were derived using the 2000–2021 SOC change map and the accumulated soil loss for each forest loss pixel annually.

The pre-processing of each carbon pool data was implemented using a High-Performance Computing workflow similar to the implementation of De Sousa et al. (2020). We mostly used terra and sf R packages to pre-process raster tiles of the carbon pools and derivation of carbon accounting tables at ecosystem and country levels. We accounted for the latitudinal effect especially affecting countries like Sweden, the Netherlands and USA, hence avoiding potential ecosystem extent underestimation.

## Compiling the carbon accounts

We then derived the carbon accounting table showing the opening and closing stocks for all carbon pools in seven accounting periods 2010–2015, 2015–2016, 2016–2017, 2017–2018, 2018–2019, 2019–2020 and 2020–2021. Compiling the accounts included deriving ecosystem extents and we opted to use the CCI Land Cover (CCI-LC) dataset, with ecosystem classifications that are consistent with SEEA

requirements (Defourny et al., 2012). This dataset belongs to the pool of EO products from the overall CCI project with other datasets of essential climate variables along with the CCI Biomass. The CCI-LC has 28 land cover classes that further allowed us to derive the following ecosystems for this study: broadleaved forest, coniferous forest, mixed forest, mangroves and wetlands, croplands, grassland and shrublands. Shown in Table S1 is the reclassification of the CCI-LC. The CCI-LC was replaced in the case of the Netherlands by the national land cover from Central Bureau of Statistics due to very low accuracy results in the validation which can be related to the heterogeneous Dutch landscape, with many small scale landscape elements not accurately captured in the CCI dataset. Lastly, both managed and unmanaged forests were included in the accounts but we were able to distinguish between the two (next section) to allow comparison with United Nations Framework Convention on Climate Change (UNFCCC) reporting.

Furthermore, emissions associated with deforestation and degradation were derived. The former were emissions in areas with forest conversion e.g., reduction in broadleaved forests in an accounting period. Degradation was attributed to the emissions in areas without forest area change but with a change in carbon stock e.g., in broadleaved forests both in 2010 and 2015. Next, we derived the carbon fluxes as the difference between the closing and opening stock, further disaggregated into net emissions and net removals (sequestration) as all negative and positive carbon flux pixels for each SEEA class, respectively. This extended table also included the net emissions driven by land use changes (forest conversions) and emissions within forests (forest degradation) based on the CCI-LC dataset. Net emissions in areas identified as forest both in the opening and closing accounting periods were attributed to forest degradation, while net emissions in areas that were forest in the opening period but non-forest in the closing
<figure><img src="image_7.png" /><figcaption>Fig. 3. Conceptual diagram showing differences and similarities between UNFCCC and the SEEA-EA framework.</figcaption></figure>

accounting periods were attributed to forest conversion. See Fig. S2 for the visualization of this part.

## Comparison with UNFCCC carbon flux reporting

The resulting carbon accounting tables were also compared with results from the UNFCCC national greenhouse inventories. Both the UNFCCC and System of Environmental-Economic Accounting (SEEA-EA) framework quantify terrestrial carbon with the IPCC carbon pool structures; however, UNFCCC aggregates net annual fluxes from “managed land” into a single national balance, whereas SEEA-EA records opening stocks, gross additions, gross reductions and closing stocks for every ecosystem class across the whole territory, enabling links to physical and monetary ecosystem-service accounts. Consequently, UNFCCC categories are activity-based (e.g., “forest-land” and “remaining forest-land”) and may omit unmanaged land, while SEEA uses land cover and extent classes and is spatially explicit—differences that can shift both the sign and magnitude of reported carbon balances (Keith et al., 2021). See Fig. 3.

We particularly compared carbon flows which is defined in the UNFCCC context as the Land Use, Land-Use Change, and Forestry (LULUCF) sector. There exists a compilation of these country reports through the Grassi et al. (2022) dataset, updated in the LULUCF datahub hosted by the EU Forest Observatory (https://forest-observatory.ec.europa.eu; Melo et al. in preparation), wherein annual net CO₂ fluxes for both developed and developing countries were compiled. The country reports had missing data (mainly for developing countries) and these were estimated using a gap-filling approach based on linear interpolation. All CO₂ values were converted into carbon by applying a 0.273 multiplier, based on the 12/44 ratio of carbon to CO₂. More information about the accessed UNFCCC country reports is shown in Table S4.

To allow comparability with the UNFCCC, all carbon pools were categorized as either managed and non-managed forests based on the Lesiv et al. (2022) forest management dataset. Using carbon pools in managed forests, we derived the classes most comparable to the UNFCCC reporting as defined and shown in Table 1. Note that the definitions followed by the Grassi et al. (2022) dataset that compiled the UNFCCC reports are also shown. Moreover, the signs of the emissions and sequestration from our estimates were switched from negative to positive and vice versa, respectively, to reflect additions (+) and removals (-) to and from the atmosphere. The respective forest areas and percentage of managed forest used were reported alongside the carbon accounts as shown in Table S3. We finally compared the sign (net source or sink) and magnitude of emission and sequestration trends from our EO-derived data with those reported by countries to the UNFCCC. The similarity in trend direction and correlation strength (r) were also analyzed.

## Validation of EO maps

Maps of the AGB, ecosystem extent and the forest management layer were validated at default map resolutions where reference data for validation were available. Land cover reference data with associated scientific publication found on Zenodo (Grassi et al., 2023; Shi et al., 2024) are employed for ecosystems, with reclassification performed to align with the SEEA typology. Additionally, forest management reference data were utilized, and validation was initiated at the original map resolutions. Accuracy metrics for these validations include confusion matrix metrics such as Overall Accuracy (OA), Producer's Accuracy (PA), and User's Accuracy (UA) for ecosystems and forest management, which are discrete variables.

To validate AGB maps, NFIs and local AGB maps derived from Airborne Laser Scanning (ALS) were used as reference data (Duncanson et al., 2021). The NFIs included Sweden (12,887 plots, 0.03–0.04 ha, systematic sampling), the Netherlands (1,562 plots, 0.03–0.04 ha, stratified random sampling), the Philippines (587 plots, 0.5 ha, systematic sampling), and Mozambique (NFI 3336 measurements based on stratified random sampling). The ALS data comprised Brazil (28,607 1-ha LiDAR footprints, largely secondary forests) and the USA (110,939 1-ha LiDAR footprints from systematic campaigns, some with repeated surveys), both harmonized following Labriere et al. (2018). All reference datasets underwent quality control filtering, where we excluded plots with land cover change after the latest measurement and LiDAR footprints overlapping only partially with map pixels. The accuracy metrics included the coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Error (ME).

$$ OA = \frac{\sum_{i=1}^{n} c_{ii}}{N} \qquad (1) $$

$$ PA = \frac{c_{ii}}{\sum_{j=1}^{n} c_{ij}} \qquad (2) $$

$$ UA = \frac{c_{ii}}{\sum_{j=1}^{n} c_{ji}} \qquad (3) $$

$$ R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2} \qquad (4) $$

$$ RMSE = \sqrt{\frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{n}} \qquad (5) $$

$$ ME = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)}{n} \qquad (6) $$

Here, let $C = [c_{ij}]$ be the confusion matrix whose rows index the reference (actual) class i and whose columns index the mapped (predicted) class j. Then $c_{ij}$ counts samples that are actually class i but classified as class j (so $c_{ii}$ are the correctly classified samples of class i), and $c_{ji}$ counts samples classified in class i but actually belonging to class j. The total number of samples is $N = \sum_{i} \sum_{j} c_{ij}$. For the regression metrics, $y_i$ and $\hat{y}_i$ denote observed and predicted values, respectively, with sample size n.

Moreover, using the multi-date validation data, we applied the uncertainty assessment and estimation following Avitabile et al. (2016) and Araza et al. (2022) i.e., a model-based approach resulting in aggregation of pixel-level errors under an error independence assumption (Harris et al., 2021). For totals over each country × ecosystem, variances were summed and square-rooted to obtain the unit-level standard deviation (SD), $\sigma_{tot} = \sqrt{\sum_{i} \sigma_{i}^{2}}$. Net flux uncertainty between two endpoints was estimated as $\sigma_{\Delta} = \sqrt{\sigma_{i}^{2} + \sigma_{j}^{2}}$. We report 95% confidence intervals as $\hat{F} \pm 1.96 \sigma$, and classify a net flux as statistically significant when its 95% CI excludes zero. When multiple carbon pools were combined, pool-level SDs were also added in quadrature.
<table><caption>**Table 1**
Comparison of UNFCCC reporting definitions with this study.</caption><thead><tr><th>UNFCCC typology</th><th>Management</th><th>UNFCCC definition</th><th>This study</th></tr></thead><tbody><tr><td>LULUCF Net</td><td>Managed and Unmanaged</td><td>Net carbon flux, gap-filled, for different land categories: FOREST LAND, DEFORESTATION, ORGANIC SOILS, OTHER LAND USES</td><td>Net carbon fluxes (flows) in managed forests and other land uses in unmanaged forests, see below</td></tr><tr><td>FOREST LAND</td><td>Managed</td><td>Includes removals in managed forests and emissions from harvest, fuelwood, etc. Excludes organic soils; includes mangrove forests</td><td>Sequestration and forest degradation included for broadleaved, coniferous, mixed, and mangrove forests in managed forests</td></tr><tr><td>DEFORESTATION</td><td>Managed</td><td>Emissions related to forest-to-non-forest conversions</td><td>Same as UNFCCC</td></tr><tr><td>ORGANIC SOILS</td><td>Managed</td><td>Fluxes from soil organic carbon, including peat fires</td><td>Fluxes from soil organic carbon, excluding those from peatlands</td></tr><tr><td>OTHER LAND USES</td><td>Managed and Unmanaged</td><td>Fluxes from other land use categories that are not forest land, including Croplands, Grasslands, Shrublands, Wetlands, Settlements, and Other Land</td><td>Fluxes from non-forest ecosystems such as Grasslands, Shrublands, and Croplands</td></tr></tbody></table>

<table><caption>**Table 2**
Combined overall accuracy metrics for ecosystems, Forest management, and AGC. These are nationwide assessment and hence include all ecosystems. Detailed confusion matrix can be found in Table S2.</caption><thead><tr><th>Country</th><th>Ecosystem total accuracy (%)</th><th>Forest management total accuracy (%)</th><th>AGC RMSE (Mg ha<sup>-1</sup>)</th><th>AGC mean error (Mg ha<sup>-1</sup>)</th><th>AGC R<sup>2</sup></th></tr></thead><tbody><tr><td>BRA</td><td>76.0</td><td>78.8</td><td>45.9</td><td>-20.80</td><td>0.43</td></tr><tr><td>MOZ</td><td>58.2</td><td>67.3</td><td>35.4</td><td>15.61</td><td>0.21</td></tr><tr><td>NLD</td><td>72.0</td><td>80.0</td><td>37.7</td><td>-12.04</td><td>0.34</td></tr><tr><td>PHL</td><td>72.2</td><td>86.4</td><td>43.8</td><td>3.25</td><td>0.17</td></tr><tr><td>SWE</td><td>52.6</td><td>92.4</td><td>24.9</td><td>2.27</td><td>0.12</td></tr><tr><td>USA</td><td>85.6</td><td>86.6</td><td>38.7</td><td>7.01</td><td>0.42</td></tr></tbody></table>

## Analysis of net fluxes by accounting interval

We evaluated the sensitivity of the normalized carbon fluxes to the length of accounting intervals by correlating annualized net fluxes against period lengths from 1 to 10 years. For each country and ecosystem category, linear models were fitted, and Pearson correlation coefficients ($r$) were derived to assess the direction and strength of the relationship. Moreover, for each dataset corresponding to different accounting intervals (1-year, 5-year, and 10-year), we calculated the annualized SD from all available carbon stock columns to capture inter-annual variability at ecosystem and country levels. In parallel, annual average net fluxes were computed by dividing total fluxes by the respective period lengths, allowing for comparability across temporal scales.

## 3. Results

### Map validation results

The results of map validations per country are presented in Table 2, while the results per ecosystem are shown in Table S3. Table 2 shows that the ecosystem map accuracy ranges from 52.6% to 85.6%. Among the ecosystem classes, the most inaccurate classification concerns the mixed forest class (see Table S3). The total accuracy of the forest management layer used is generally higher, ranging from 67.3% to 92.4%, which is reasonable given that the layer consists of two classes. Non-forest classes such as grassland and shrublands have accuracy ranging from 21.4% to 99.7%. In terms of above-ground carbon (AGC), root mean square error (RMSE) values range from 24.9 to 45.9 Mg ha⁻¹, with varying degrees of mean error and correlation (0.12 to 0.43 R²). These variations can also be driven by the reference data used; for instance, Brazil and the USA employed Airborne Laser Scanning (ALS)-based reference data and achieved R² > 0.4, while national forest inventories (NFIs) were used for other countries, resulting in correlation values ranging from 0.12 to 0.21 R². See Table S2 for and Fig. S4 for the confusion matrix and scatterplots of the validation exercises.

### The compiled carbon accounts of all carbon pools in six countries

Table 3 presents the carbon (C) accounts for multiple ecosystems across six countries, spanning seven accounting periods from 2010 to 2021. The table shows varying trends in carbon fluxes across countries and ecosystem types. Countries like Mozambique, the Netherlands and the Philippines show more stable or modestly increasing carbon trends in several ecosystem categories. In contrast, the rest of the countries generally exhibit declining carbon stocks, particularly in dominant forest types. Broadleaved forests in the case of tropical countries demonstrate declines in carbon stocks, while coniferous forests in temperate countries show substantial losses. However, some ecosystems, such as grassland, shrublands and mixed forests, show minor increases in C stocks in certain periods and countries, suggesting possible land cover transitions or ecosystem recovery. A more detailed breakdown in Table S3 reveals that emissions from forest degradation are significantly lower than those from deforestation. It is noted that our assessment of emissions due to forest degradation are net numbers, i.e. they include degradation in some areas and recovery in other areas. These estimates are supported by annual ecosystem extent data that allow attribution of carbon flows to specific land use processes and changes in ecosystem area. However, the low reported values also reflect definitional differences.

The uncertainties (SDs) reported alongside each net flux value in the carbon accounts are small for low-biomass ecosystems (e.g., Shrublands, Wetlands; typically < 1 Gg in Mozambique, the Netherlands and the Philippines) but markedly larger for Brazil Broadleaved and USA Coniferous and Broadleaved. Moreover, in Fig. S6, most net flux estimates cluster near zero with 95% confidence intervals (CIs) crossing zero, indicating no change. Notable exceptions are Brazil's broadleaved forests (large, consistently emissions with CIs excluding zero) and Sweden's coniferous forests (persistent moderate emissions). The Netherlands and the Philippines show small, near-zero fluxes; Mozambique exhibits a strong early emission (2010–2015) that attenuates, and the United States shows mixed, mostly weak signals (coniferous forests tending negative, occasional positive grassland fluxes).

### Carbon stocks and fluxes among different carbon pools

Fig. 4 shows that the distribution of carbon pools differed among the six countries during the same accounting periods. The majority of the
<caption>Table 3</caption>

<table><caption>Carbon accounts (Gg) of the target countries in all possible accounting periods. Accounts include both managed and non-managed forests.</caption><thead><tr><th>Country</th><th>Ecosystems</th><th>Stocks_2010</th><th>NetFlux_2010_2015</th><th>Stocks_2015</th><th>NetFlux_2015_2016</th><th>Stocks_2016</th><th>NetFlux_2016_2017</th><th>Stocks_2017</th><th>NetFlux_2017_2018</th><th>Stocks_2018</th><th>NetFlux_2018_2019</th><th>Stocks_2019</th><th>NetFlux_2019_2020</th><th>Stocks_2020</th><th>NetFlux_2020_2021</th></tr></thead><tbody><tr><td>BRA</td><td>Broadleaved forest</td><td>77 158.0</td><td>-89.57 ± 71.51</td><td>76 262.4</td><td>-179.1 ± 69.76</td><td>76 083.3</td><td>-109.7 ± 69.40</td><td>75 973.5</td><td>-102.4 ± 69.60</td><td>75 871.1</td><td>-81.7 ± 68.85</td><td>75 789.4</td><td>-94.6 ± 69.01</td><td>75 694.9</td><td>-167.0 ± 69.09</td></tr><tr><td>BRA</td><td>Coniferous forest</td><td>5.3</td><td>-0.1 ± 0.34</td><td>5.1</td><td>0.1 ± 0.30</td><td>5.2</td><td>0.0 ± 0.29</td><td>5.2</td><td>-0.0 ± 0.30</td><td>5.2</td><td>0.0 ± 0.29</td><td>5.2</td><td>0.0 ± 0.29</td><td>5.3</td><td>0.4 ± 0.29</td></tr><tr><td>BRA</td><td>Croplands</td><td>5569.3</td><td>-116.6 ± 2.54</td><td>5452.7</td><td>-52.8 ± 2.28</td><td>5399.9</td><td>-31.0 ± 2.23</td><td>5368.9</td><td>-7.6 ± 2.27</td><td>5361.3</td><td>-37.2 ± 2.19</td><td>5324.2</td><td>-28.8 ± 2.18</td><td>5299.4</td><td>-19.1 ± 2.22</td></tr><tr><td>BRA</td><td>Grassland</td><td>7738.5</td><td>-33.8 ± 7.78</td><td>7704.7</td><td>-42.5 ± 7.24</td><td>7662.3</td><td>-26.8 ± 7.15</td><td>7635.4</td><td>8.1 ± 7.27</td><td>7643.6</td><td>-35.0 ± 7.08</td><td>7608.5</td><td>-12.2 ± 7.11</td><td>7596.4</td><td>-27.2 ± 7.24</td></tr><tr><td>BRA</td><td>Mixed forest</td><td>2067.7</td><td>-8.2 ± 7.90</td><td>2059.5</td><td>3.2 ± 7.27</td><td>2062.7</td><td>1.4 ± 7.18</td><td>2064.1</td><td>23.6 ± 7.34</td><td>2087.8</td><td>40.1 ± 7.16</td><td>2127.9</td><td>11.9 ± 7.22</td><td>2139.8</td><td>25.0 ± 7.31</td></tr><tr><td>BRA</td><td>Sparce & Rare</td><td>22.0</td><td>-0.1 ± 0.22</td><td>22.0</td><td>0.3 ± 0.19</td><td>22.3</td><td>-0.1 ± 0.19</td><td>22.2</td><td>-0.1 ± 0.19</td><td>22.2</td><td>0.1 ± 0.18</td><td>22.3</td><td>-0.1 ± 0.18</td><td>22.2</td><td>0.3 ± 0.18</td></tr><tr><td>BRA</td><td>Wetlands & Mangroves</td><td>3022.0</td><td>-30.2 ± 6.45</td><td>2991.8</td><td>-10.3 ± 6.05</td><td>2981.5</td><td>-0.4 ± 5.99</td><td>2981.1</td><td>-0.4 ± 6.06</td><td>2980.7</td><td>0.4 ± 5.95</td><td>2981.1</td><td>-6.7 ± 5.97</td><td>2974.4</td><td>-6.7 ± 5.95</td></tr><tr><td>MOZ</td><td>Broadleaved forest</td><td>7 951.4</td><td>-76.4 ± 3.40</td><td>7 836.9</td><td>-24.2 ± 3.42</td><td>7 800.6</td><td>-14.9 ± 3.32</td><td>7 778.2</td><td>-14.3 ± 3.34</td><td>7 756.8</td><td>-16.3 ± 3.21</td><td>7 732.3</td><td>-30.0 ± 3.27</td><td>7 687.3</td><td>-23.1 ± 3.39</td></tr><tr><td>MOZ</td><td>Coniferous forest</td><td>0.0</td><td>0.0 ± 0.00</td><td>0.0</td><td>0.0 ± 0.00</td><td>0.1</td><td>0.0 ± 0.00</td><td>0.1</td><td>0.0 ± 0.00</td><td>0.1</td><td>0.0 ± 0.00</td><td>0.1</td><td>0.0 ± 0.00</td><td>0.1</td><td>0.0 ± 0.00</td></tr><tr><td>MOZ</td><td>Croplands</td><td>807.6</td><td>-7.7 ± 0.56</td><td>796.0</td><td>-6.1 ± 0.44</td><td>786.8</td><td>-2.3 ± 0.42</td><td>783.3</td><td>1.4 ± 0.45</td><td>785.4</td><td>0.4 ± 0.42</td><td>786.0</td><td>-2.4 ± 0.43</td><td>782.5</td><td>0.7 ± 0.44</td></tr><tr><td>MOZ</td><td>Grassland</td><td>921.9</td><td>-6.7 ± 0.74</td><td>911.9</td><td>-5.7 ± 0.50</td><td>903.4</td><td>-0.6 ± 0.49</td><td>902.4</td><td>3.5 ± 0.48</td><td>907.7</td><td>0.0 ± 0.46</td><td>907.8</td><td>-0.0 ± 0.47</td><td>907.7</td><td>5.1 ± 0.48</td></tr><tr><td>MOZ</td><td>Mixed forest</td><td>76.3</td><td>0.2 ± 0.27</td><td>76.6</td><td>1.2 ± 0.29</td><td>78.5</td><td>0.1 ± 0.28</td><td>78.6</td><td>0.8 ± 0.29</td><td>79.9</td><td>1.5 ± 0.28</td><td>82.1</td><td>-0.2 ± 0.30</td><td>81.9</td><td>0.1 ± 0.31</td></tr><tr><td>MOZ</td><td>Shrublands</td><td>1 897.1</td><td>-21.2 ± 0.90</td><td>1 865.4</td><td>-7.8 ± 0.84</td><td>1 853.6</td><td>-1.6 ± 0.83</td><td>1 851.2</td><td>5.8 ± 0.87</td><td>1 859.8</td><td>1.1 ± 0.86</td><td>1 861.5</td><td>-2.3 ± 0.88</td><td>1 858.0</td><td>6.9 ± 0.89</td></tr><tr><td>MOZ</td><td>Wetlands & Mangroves</td><td>454.8</td><td>-5.2 ± 0.36</td><td>447.0</td><td>-5.6 ± 0.37</td><td>438.5</td><td>1.6 ± 0.36</td><td>441.0</td><td>0.2 ± 0.36</td><td>442.0</td><td>0.5 ± 0.35</td><td>442.8</td><td>0.5 ± 0.36</td><td>443.5</td><td>1.3 ± 0.35</td></tr><tr><td>NLD</td><td>Broadleaved forest</td><td>29.2</td><td>0.3 ± 0.46</td><td>29.6</td><td>0.6 ± 0.45</td><td>30.5</td><td>-0.3 ± 0.45</td><td>30.1</td><td>0.1 ± 0.45</td><td>30.3</td><td>0.4 ± 0.47</td><td>30.8</td><td>0.2 ± 0.45</td><td>31.2</td><td>-0.1 ± 0.43</td></tr><tr><td>NLD</td><td>Coniferous forest</td><td>111.0</td><td>0.1 ± 0.85</td><td>111.2</td><td>0.0 ± 0.80</td><td>111.3</td><td>0.2 ± 0.81</td><td>111.6</td><td>-0.3 ± 0.82</td><td>111.2</td><td>0.5 ± 0.87</td><td>112.0</td><td>-0.5 ± 0.84</td><td>111.2</td><td>-0.6 ± 0.77</td></tr><tr><td>NLD</td><td>Croplands</td><td>204.7</td><td>6.1 ± 0.40</td><td>213.8</td><td>-8.5 ± 0.43</td><td>201.1</td><td>3.5 ± 0.40</td><td>206.2</td><td>-5.2 ± 0.37</td><td>198.5</td><td>-3.9 ± 0.34</td><td>192.7</td><td>-0.9 ± 0.33</td><td>191.3</td><td>0.0 ± 0.33</td></tr><tr><td>NLD</td><td>Grassland</td><td>784.8</td><td>8.3 ± 0.74</td><td>797.2</td><td>-4.4 ± 0.77</td><td>790.7</td><td>5.2 ± 0.76</td><td>798.4</td><td>-8.4 ± 0.73</td><td>785.8</td><td>-2.8 ± 0.71</td><td>781.6</td><td>-3.3 ± 0.71</td><td>776.6</td><td>-1.6 ± 0.70</td></tr><tr><td>NLD</td><td>Mixed forest</td><td>103.5</td><td>0.6 ± 0.85</td><td>104.4</td><td>0.4 ± 0.83</td><td>105.0</td><td>0.3 ± 0.83</td><td>105.5</td><td>0.5 ± 0.82</td><td>106.2</td><td>1.2 ± 0.83</td><td>108.1</td><td>0.1 ± 0.81</td><td>108.2</td><td>-0.2 ± 0.79</td></tr><tr><td>NLD</td><td>Shrublands</td><td>0.1</td><td>0.0 ± 0.02</td><td>0.1</td><td>-0.0 ± 0.02</td><td>0.1</td><td>-0.0 ± 0.01</td><td>0.1</td><td>-0.0 ± 0.01</td><td>0.1</td><td>0.0 ± 0.01</td><td>0.1</td><td>-0.0 ± 0.01</td><td>0.1</td><td>0.0 ± 0.01</td></tr><tr><td>NLD</td><td>Wetlands & Mangroves</td><td>22.5</td><td>0.1 ± -</td><td>22.6</td><td>-0.2 ± -</td><td>22.4</td><td>-0.0 ± -</td><td>22.4</td><td>-0.2 ± -</td><td>22.1</td><td>-0.1 ± -</td><td>21.9</td><td>-0.1 ± -</td><td>21.7</td><td>0.1 ± -</td></tr><tr><td>PHL</td><td>Broadleaved forest</td><td>4 137.0</td><td>43.1 ± 5.60</td><td>4 201.6</td><td>-1.5 ± 4.85</td><td>4 199.4</td><td>-17.9 ± 4.86</td><td>4 172.6</td><td>0.2 ± 4.93</td><td>4 172.9</td><td>-12.3 ± 4.61</td><td>4 154.4</td><td>-9.1 ± 4.74</td><td>4 140.8</td><td>-4.6 ± 4.89</td></tr><tr><td>PHL</td><td>Coniferous forest</td><td>0.0</td><td>0.0 ± 0.00</td><td>0.0</td><td>0.0 ± 0.00</td><td>0.0</td><td>-0.0 ± 0.00</td><td>0.0</td><td>0.0 ± 0.00</td><td>0.0</td><td>0.0 ± 0.00</td><td>0.0</td><td>-0.0 ± 0.00</td><td>0.0</td><td>-0.0 ± 0.00</td></tr><tr><td>PHL</td><td>Croplands</td><td>2 680.4</td><td>-38.1 ± 0.70</td><td>2 623.3</td><td>1.6 ± 0.57</td><td>2 625.7</td><td>15.9 ± 0.61</td><td>2 649.5</td><td>-13.4 ± 0.60</td><td>2 629.4</td><td>-7.6 ± 0.54</td><td>2 618.0</td><td>10.1 ± 0.57</td><td>2 633.2</td><td>-2.8 ± 0.60</td></tr><tr><td>PHL</td><td>Grassland</td><td>822.3</td><td>-7.4 ± 0.71</td><td>811.2</td><td>1.6 ± 0.57</td><td>813.5</td><td>3.1 ± 0.61</td><td>818.1</td><td>-4.4 ± 0.62</td><td>811.5</td><td>0.3 ± 0.55</td><td>811.9</td><td>18.4 ± 0.63</td><td>839.5</td><td>-15.5 ± 0.66</td></tr><tr><td>PHL</td><td>Mixed forest</td><td>0.1</td><td>0.0 ± 0.02</td><td>0.1</td><td>0.0 ± 0.01</td><td>0.1</td><td>-0.0 ± 0.02</td><td>0.1</td><td>0.0 ± 0.02</td><td>0.1</td><td>0.0 ± 0.02</td><td>0.1</td><td>-0.0 ± 0.02</td><td>0.1</td><td>0.0 ± 0.02</td></tr><tr><td>PHL</td><td>Shrublands</td><td>40.7</td><td>0.2 ± 0.48</td><td>41.0</td><td>-0.1 ± 0.39</td><td>40.8</td><td>0.4 ± 0.39</td><td>41.4</td><td>0.5 ± 0.41</td><td>42.2</td><td>0.6 ± 0.38</td><td>43.1</td><td>0.1 ± 0.40</td><td>43.2</td><td>-0.3 ± 0.41</td></tr><tr><td>PHL</td><td>Wetlands & Mangroves</td><td>217.7</td><td>2.3 ± 0.91</td><td>221.1</td><td>-0.9 ± 0.77</td><td>219.8</td><td>-0.6 ± 0.76</td><td>218.9</td><td>-0.2 ± 0.78</td><td>218.6</td><td>-0.5 ± 0.73</td><td>218.0</td><td>-1.0 ± 0.75</td><td>216.5</td><td>-0.8 ± 0.75</td></tr><tr><td>SWE</td><td>Broadleaved forest</td><td>941.1</td><td>3.0 ± 1.16</td><td>945.6</td><td>-2.5 ± 1.20</td><td>942.0</td><td>-0.7 ± 1.18</td><td>941.0</td><td>-13.9 ± 1.18</td><td>920.1</td><td>-7.3 ± 1.20</td><td>909.1</td><td>3.2 ± 1.17</td><td>913.9</td><td>-0.6 ± 1.20</td></tr><tr><td>SWE</td><td>Coniferous forest</td><td>20 369.0</td><td>-171.1 ± 6.62</td><td>20 112.3</td><td>-74.3 ± 7.14</td><td>20 009.0</td><td>-117.4 ± 7.08</td><td>19 824.8</td><td>-256.5 ± 6.93</td><td>19 440.1</td><td>-257.4 ± 6.97</td><td>19 054.0</td><td>-73.8 ± 6.83</td><td>18 943.3</td><td>-44.5 ± 6.91</td></tr><tr><td>SWE</td><td>Croplands</td><td>1681.9</td><td>35.2 ± 0.97</td><td>1734.7</td><td>-1.7 ± 1.03</td><td>1732.1</td><td>-0.5 ± 1.03</td><td>1731.3</td><td>-3.0 ± 1.04</td><td>1726.8</td><td>-2.8 ± 1.08</td><td>1722.5</td><td>2.5 ± 1.05</td><td>1726.2</td><td>-4.8 ± 1.06</td></tr><tr><td>SWE</td><td>Grassland</td><td>1 366.9</td><td>9.3 ± 0.52</td><td>1 380.8</td><td>15.2 ± 0.56</td><td>1 403.5</td><td>19.7 ± 0.59</td><td>1 433.1</td><td>59.1 ± 0.67</td><td>1 521.8</td><td>37.2 ± 0.76</td><td>1 577.6</td><td>2.5 ± 0.79</td><td>1 581.4</td><td>-0.6 ± 0.79</td></tr><tr><td>SWE</td><td>Mixed forest</td><td>2 412.7</td><td>38.9 ± 2.06</td><td>247.1</td><td>7.6 ± 2.21</td><td>248.2</td><td>17.4 ± 2.21</td><td>250.85</td><td>91.5 ± 2.26</td><td>2645.7</td><td>68.2 ± 2.35</td><td>2747.0</td><td>11.6 ± 2.36</td><td>2765.3</td><td>3.0 ± 2.39</td></tr><tr><td>SWE</td><td>Shrublands</td><td>30.9</td><td>2.1 ± 0.12</td><td>34.1</td><td>0.0 ± 0.13</td><td>34.2</td><td>0.5 ± 0.13</td><td>34.9</td><td>2.0 ± 0.15</td><td>38.0</td><td>2.6 ± 0.16</td><td>41.8</td><td>0.3 ± 0.17</td><td>42.2</td><td>-0.1 ± 0.17</td></tr><tr><td>SWE</td><td>Wetlands & Mangroves</td><td>1 976.8</td><td>-4.9 ± 0.02</td><td>1 969.5</td><td>7.0 ± 0.02</td><td>1 980.0</td><td>13.1 ± 0.02</td><td>1 999.6</td><td>55.0 ± 0.02</td><td>2 082.1</td><td>94.2 ± 0.02</td><td>2 223.4</td><td>1.7 ± 0.02</td><td>2225.9</td><td>6.1 ± 0.02</td></tr><tr><td>USA</td><td>Broadleaved forest</td><td>12 707.8</td><td>-35.2 ± 30.78</td><td>12 672.7</td><td>-1.4 ± 30.56</td><td>12 671.2</td><td>-7.1 ± 31.08</td><td>12 664.1</td><td>-185.0 ± 31.12</td><td>12 479.1</td><td>-107.9 ± 30.94</td><td>12 371.2</td><td>59.0 ± 31.60</td><td>12 430.2</td><td>-93.4 ± 31.74</td></tr><tr><td>USA</td><td>Coniferous forest</td><td>17 752.5</td><td>-398.7 ± 28.73</td><td>17 353.8</td><td>-145.8 ± 28.33</td><td>17 208.0</td><td>-111.8 ± 27.61</td><td>17 096.1</td><td>-146.3 ± 27.96</td><td>16 949.9</td><td>-205.7 ± 27.64</td><td>16 744.1</td><td>-71.1 ± 27.68</td><td>16 673.0</td><td>-91.1 ± 27.77</td></tr><tr><td>USA</td><td>Croplands</td><td>1 025.2</td><td>-14.9 ± 2.39</td><td>1 010.3</td><td>-4.3 ± 2.31</td><td>1 006.0</td><td>-2.9 ± 2.25</td><td>1 003.1</td><td>0.5 ± 2.28</td><td>1 003.6</td><td>1.4 ± 2.23</td><td>1 005.1</td><td>-0.4 ± 2.25</td><td>1 004.7</td><td>-2.3 ± 2.28</td></tr><tr><td>USA</td><td>Grassland</td><td>21 105.3</td><td>-114.9 ± 12.09</td><td>20 990.3</td><td>34.3 ± 11.73</td><td>21 024.6</td><td>-67.0 ± 11.54</td><td>20 956.7</td><td>123.0 ± 12.00</td><td>19 03 ± 12.00</td><td>11.9 ± 12.00</td><td>21 189.0</td><td>-27.0 ± 12.24</td><td>21 162.0</td><td>32.1 ± 12.40</td></tr><tr><td>USA</td><td>Mixed forest</td><td>4 221.9</td><td>-4.2 ± 17.60</td><td>4 217.7</td><td>-21.7 ± 17.70</td><td>4 195.9</td><td>-20.7 ± 17.67</td><td>4 175.2</td><td>6.9 ± 17.79</td><td>4 182.1</td><td>-14.1 ± 17.74</td><td>4 167.9</td><td>13.0 ± 17.97</td><td>4 181.0</td><td>-3.7 ± 18.08</td></tr><tr><td>USA</td><td>Shrublands</td><td>6 062.7</td><td>20.0 ± 5.50</td><td>6 082.7</td><td>-6.4 ± 5.36</td><td>6 076.3</td><td>8.2 ± 5.22</td><td>6 084.6</td><td>21.6 ± 5.51</td><td>6 106.1</td><td>37.0 ± 5.50</td><td>6 143.2</td><td>4.7 ± 5.68</td><td>6 147.9</td><td>-0.1 ± 5.78</td></tr><tr><td>USA</td><td>Wetlands & Mangroves</td><td>2579.8</td><td>2.0 ± 10.66</td><td>2581.8</td><td>-0.1 ± 10.73</td><td>2581.7</td><td>-1.3 ± 10.65</td><td>2580.4</td><td>-4.4 ± 10.53</td><td>2576.0</td><td>-0.3 ± 10.43</td><td>2575.7</td><td>0.3 ± 10.35</td><td>2576.0</td><td>-10.5 ± 10.35</td></tr></tbody></table>

countries have soil carbon as the most abundant carbon pool i.e., the Netherlands, Mozambique, Sweden and the USA. One explanation for the dominance of soil carbon stocks particularly in non-tropical countries is the slow decomposition of organic matter in the soil because of the lower temperature and moist environment (Yu et al., 2022). For Brazil, the above-ground carbon pool constitutes more than half the total carbon stocks of the country and also exhibit relatively higher below-ground carbon and litter. Another figure shown in Fig. 4 is about the net carbon fluxes of each carbon pool over different accounting periods. Here it can be observed that the above-ground carbon pool exhibits the largest changes. Non-tropical countries USA and Sweden show considerable soil organic carbon (SOC) fluxes in the 2017–2018 and 2018–2019 periods. Except for that, the annual carbon fluxes are steady. It can also be visualized in the figure which countries are more of a carbon source than sink throughout the accounting periods e.g., Brazil and Sweden.

### Effects of accounting period length on carbon fluxes

Fig. 5 shows that emission and sequestration rates are negatively correlated with accounting period length, meaning inter-annual variability is high for one-year periods but becomes increasingly smoothed over longer intervals. Emission trends are consistently strong across countries ($|r| = 0.57–0.85$), with Mozambique and the Netherlands showing the strongest correlations, while Sweden and the Philippines exhibit weaker persistence of variability. Sequestration trends are more heterogeneous, with the Netherlands, the Philippines, and the USA exhibiting strong correlations ($|r| = 0.78–0.81$), Sweden and Mozambique showing moderate correlations, and Brazil displaying only weak correlation. Overall, these indicate that annual accounts capture important short-term dynamics, while multi-year periods reduce variability but risk masking year-to-year changes. Fig. S5 shows that the variability

(SD) of annualized net fluxes decreases as accounting periods lengthen from 1-year to 5-year and 10-year intervals, confirming that short-term accounts capture large inter-annual fluctuations while longer periods smooth these dynamics. The largest uncertainties are concentrated in dominant ecosystems such as broadleaved forests in Brazil and the Philippines, and coniferous forests in Sweden and the USA, where strong signals of both sequestration (positive values above zero) and emissions (negative values below zero) are observed. Mozambique is an exception, with relatively modest variability despite showing clear emission signals from broadleaved forests.

### Comparison of carbon fluxes between carbon accounting frameworks

Table 4 shows the comparison of the carbon flows between the United Nations Framework Convention on Climate Change (UNFCCC) reports and this EO-based study. The comparison is limited to carbon flows since the UNFCCC focuses on fluxes, not stocks. The general observation is that in one-year accounting periods, most countries have not recorded any flows based on their UNFCCC reports. This study, on the other hand, captures most flows including soil emissions. Looking at specific flows that have been reported to the UNFCCC in Table S4, no changes in the reported annual information can be observed in the sequestered carbon of forest land in Mozambique, the deforested areas in the Philippines, organic soils in the three tropical countries and other land uses in Mozambique and the Philippines. Contrary, EO-based accounts detect way more fluxes both deforestation and sequestration in forests, see “FORESTLAND” and “DEFORESTATION” rows. The table also reveals statistically significant fluxes in Sweden (2015–2019) and the USA (2015–2018), while Brazil is negative only in the earliest period (opening 2010). Mozambique remains significantly negative through 2019 but not in 2020; the Netherlands stays near zero, and the
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Closing carbon stocks (above) and annual carbon fluxes (below) for each carbon pool of the six case countries over accounting periods. Note that 2010–2015 is annualized here i.e., total fluxes divided by five. Note that the unit of carbon here is C and not CO2.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Correlation between accounting period and net fluxes (net emission or net sequestration) for all countries.</figcaption></figure>
<caption>**Table 4**</caption>

Comparison of the carbon accounts net fluxes (emissions-sequestrations) between UNFCCC and this study in managed forests. This study includes uncertainty estimates and an indication whether the fluxes indicate uncertainty where fluxes indicate high uncertainty, i.e. zero values included in the range within 1 SD from the fluxes (red text, blue otherwise). Note that UNFCCC reports are only until 2019–2020 period in Grassi et al. (2022) and the negative signs refer to sequestration and otherwise if emission for consistency with the signage used in UNFCCC reporting. The table presents stocks and flows modeled based on EO data for managed forests only to allow comparison with UNFCCC reporting, refer to Table 3. Longer table with comparisons of carbon stocks are appended.

<table><thead><tr><th rowspan="2">Country</th><th rowspan="2">Class</th><th colspan="6">This study (Gg, mean ± SD)</th><th colspan="6">UNFCCC (Gg)</th></tr><tr><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th><th>2020</th><th>2015</th><th>2016</th><th>2017</th><th>2018</th><th>2019</th><th>2020</th></tr></thead><tbody><tr><td colspan="14" style="text-align:center; font-weight:bold;">LULUCF net</td></tr><tr><td>BRA</td><td>LULUCF net</td><td>-52.32 ± 20.22</td><td>-52.11 ± 19.71</td><td>-30.44 ± 19.61</td><td>-22.15 ± 19.67</td><td>-11.52 ± 19.45</td><td>-25.02 ± 19.50</td><td>91.35</td><td>99.54</td><td>99.54</td><td>99.54</td><td>99.54</td><td>99.54</td></tr><tr><td>MOZ</td><td>LULUCF net</td><td>-13.83 ± 2.92</td><td>-24.31 ± 2.93</td><td>-11.25 ± 2.85</td><td>-10.84 ± 2.86</td><td>-12.12 ± 2.76</td><td>-25.26 ± 2.81</td><td>10.64</td><td>10.64</td><td>10.64</td><td>10.64</td><td>10.64</td><td>10.64</td></tr><tr><td>NLD</td><td>LULUCF net</td><td>0.22 ± 1.29</td><td>0.89 ± 1.24</td><td>0.27 ± 1.24</td><td>0.10 ± 1.25</td><td>1.97 ± 1.30</td><td>-0.30 ± 1.25</td><td>1.09</td><td>1.09</td><td>1.09</td><td>1.09</td><td>0.82</td><td>0.82</td></tr><tr><td>PHL</td><td>LULUCF net</td><td>4.72 ± 2.95</td><td>-1.24 ± 2.55</td><td>-9.59 ± 2.56</td><td>0.03 ± 2.59</td><td>-6.65 ± 2.43</td><td>-5.23 ± 2.50</td><td>-28.63</td><td>-28.63</td><td>-28.63</td><td>-28.63</td><td>-28.63</td><td>-28.63</td></tr><tr><td>SWE</td><td>LULUCF net</td><td>-22.52 ± 5.91</td><td>-52.20 ± 6.36</td><td>-73.61 ± 6.30</td><td>-104.07 ± 6.20</td><td>-86.03 ± 6.26</td><td>-48.16 ± 6.15</td><td>-10.91</td><td>-10.91</td><td>-9.82</td><td>-9.82</td><td>-10.36</td><td>-11.18</td></tr><tr><td>USA</td><td>LULUCF net</td><td>-78.49 ± 42.18</td><td>-152.13 ± 41.88</td><td>-126.91 ± 41.77</td><td>-295.98 ± 42.00</td><td>-295.20 ± 41.68</td><td>1.06 ± 42.16</td><td>-204.25</td><td>-235.07</td><td>-225.52</td><td>-220.61</td><td>-207.52</td><td>-221.43</td></tr><tr><td colspan="14" style="text-align:center; font-weight:bold;">Breakdown</td></tr><tr><td colspan="14" style="text-align:center; font-weight:bold;">FOREST LAND</td></tr><tr><td>BRA</td><td>FOREST LAND</td><td>-166.28</td><td>-213.32</td><td>-114.71</td><td>-120.52</td><td>-154.14</td><td>-113.55</td><td>-107.72</td><td>-109.08</td><td>-109.08</td><td>-109.08</td><td>-109.08</td><td>-109.08</td></tr><tr><td>MOZ</td><td>FOREST LAND</td><td>-7.98</td><td>-12.07</td><td>-5.09</td><td>-6.38</td><td>-5.39</td><td>-8.90</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>NLD</td><td>FOREST LAND</td><td>-0.51</td><td>-1.88</td><td>-1.29</td><td>-2.86</td><td>-1.42</td><td>-1.21</td><td>-0.55</td><td>-0.55</td><td>-0.55</td><td>-0.55</td><td>-0.55</td><td>-0.55</td></tr><tr><td>PHL</td><td>FOREST LAND</td><td>-3.28</td><td>-0.99</td><td>-5.61</td><td>-1.55</td><td>-3.59</td><td>-5.36</td><td>-28.63</td><td>-28.63</td><td>-28.63</td><td>-28.63</td><td>-28.63</td><td>-28.63</td></tr><tr><td>SWE</td><td>FOREST LAND</td><td>-15.56</td><td>-29.25</td><td>-47.64</td><td>-134.76</td><td>-124.07</td><td>-25.05</td><td>-14.45</td><td>-13.36</td><td>-13.64</td><td>-13.64</td><td>-13.64</td><td>-14.45</td></tr><tr><td>USA</td><td>FOREST LAND</td><td>-116.10</td><td>-205.12</td><td>-195.12</td><td>-308.42</td><td>-333.61</td><td>-120.39</td><td>-199.07</td><td>-226.07</td><td>-215.71</td><td>-212.16</td><td>-200.16</td><td>-209.71</td></tr><tr><td colspan="14" style="text-align:center; font-weight:bold;">DEFORESTATION</td></tr><tr><td>BRA</td><td>DEFORESTATION</td><td>85.45</td><td>105.38</td><td>24.01</td><td>62.80</td><td>100.82</td><td>50.26</td><td>197.98</td><td>209.98</td><td>209.98</td><td>209.98</td><td>209.98</td><td>209.98</td></tr><tr><td>MOZ</td><td>DEFORESTATION</td><td>2.68</td><td>6.03</td><td>-2.25</td><td>2.85</td><td>0.20</td><td>2.98</td><td>10.64</td><td>10.64</td><td>10.64</td><td>10.64</td><td>10.64</td><td>10.64</td></tr><tr><td>NLD</td><td>DEFORESTATION</td><td>0.71</td><td>2.01</td><td>1.67</td><td>1.98</td><td>1.28</td><td>0.96</td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.27</td></tr><tr><td>PHL</td><td>DEFORESTATION</td><td>3.44</td><td>-0.57</td><td>4.02</td><td>0.08</td><td>1.96</td><td>8.02</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>SWE</td><td>DEFORESTATION</td><td>6.41</td><td>19.52</td><td>35.17</td><td>106.45</td><td>108.53</td><td>15.32</td><td>1.09</td><td>0.82</td><td>0.55</td><td>0.82</td><td>0.82</td><td>0.55</td></tr><tr><td>USA</td><td>DEFORESTATION</td><td>69.92</td><td>174.26</td><td>125.34</td><td>244.41</td><td>273.60</td><td>86.95</td><td>34.63</td><td>34.63</td><td>34.63</td><td>34.63</td><td>34.63</td><td>34.63</td></tr><tr><td colspan="14" style="text-align:center; font-weight:bold;">ORGANIC SOILS</td></tr><tr><td>BRA</td><td>ORGANIC SOILS</td><td>48.84</td><td>93.59</td><td>83.70</td><td>54.94</td><td>44.76</td><td>56.97</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>MOZ</td><td>ORGANIC SOILS</td><td>3.29</td><td>4.27</td><td>7.15</td><td>3.64</td><td>4.89</td><td>4.71</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>NLD</td><td>ORGANIC SOILS</td><td>-0.03</td><td>-0.63</td><td>0.04</td><td>0.00</td><td>-0.08</td><td>0.02</td><td>1.36</td><td>1.36</td><td>1.36</td><td>1.36</td><td>1.36</td><td>1.36</td></tr><tr><td>PHL</td><td>ORGANIC SOILS</td><td>0.05</td><td>1.62</td><td>1.36</td><td>1.07</td><td>1.48</td><td>0.89</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>SWE</td><td>ORGANIC SOILS</td><td>7.67</td><td>7.07</td><td>7.86</td><td>10.28</td><td>10.54</td><td>7.14</td><td>2.45</td><td>2.45</td><td>2.45</td><td>2.45</td><td>2.45</td><td>2.45</td></tr><tr><td>USA</td><td>ORGANIC SOILS</td><td>28.84</td><td>33.62</td><td>38.52</td><td>65.16</td><td>49.91</td><td>31.53</td><td>12.54</td><td>12.54</td><td>12.82</td><td>12.27</td><td>12.00</td><td>12.00</td></tr><tr><td colspan="14" style="text-align:center; font-weight:bold;">OTHER LAND USES</td></tr><tr><td>BRA</td><td>OTHER LAND USES</td><td>34.35</td><td>86.74</td><td>49.59</td><td>14.26</td><td>75.98</td><td>33.98</td><td>0.82</td><td>-1.09</td><td>-1.09</td><td>-1.09</td><td>-1.09</td><td>-1.09</td></tr><tr><td>MOZ</td><td>OTHER LAND USES</td><td>6.95</td><td>13.50</td><td>2.27</td><td>-9.31</td><td>-1.14</td><td>2.33</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>NLD</td><td>OTHER LAND USES</td><td>-2.07</td><td>4.35</td><td>-5.17</td><td>8.38</td><td>2.84</td><td>3.35</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>PHL</td><td>OTHER LAND USES</td><td>1.79</td><td>-1.45</td><td>-3.47</td><td>3.95</td><td>-0.85</td><td>-18.50</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>SWE</td><td>OTHER LAND USES</td><td>-2.85</td><td>-15.21</td><td>-20.20</td><td>-61.14</td><td>-39.79</td><td>-2.76</td><td>0.27</td><td>0.27</td><td>0.27</td><td>0.55</td><td>0</td><td>0</td></tr><tr><td>USA</td><td>OTHER LAND USES</td><td>23.74</td><td>-27.89</td><td>59.73</td><td>-144.60</td><td>-146.31</td><td>22.27</td><td>-52.09</td><td>-56.18</td><td>-56.99</td><td>-55.36</td><td>-53.72</td><td>-58.63</td></tr></tbody></table>

Philippines shows mixed behavior with significant negatives in 2016 and 2018–2019.

Fig. 6 shows the UNFCCC estimates are comparatively stable across years (1-, 5-, 10-year), with small shifts in means and narrow error bars as countries often register little or no annual change (Table S4). In contrast, EO-based estimates show larger inter-annual variability, most evident in the annual accounting where standard deviations are wider, reflecting sensitivity to year-specific dynamics (e.g., climate anomalies, disturbances, activity changes and potential biases in the analysis of EO data). Agreement between sources is mixed i.e., in some cases the sign and magnitude are similar, while in others the EO-based means diverge from UNFCCC values. Notably, the EO-based series exhibits decreasing variability and a clearer stabilization of the mean as the accounting period lengthens. When compared with a version including unmanaged forests, these correlations and agreement decrease, which indicates that comparability between UNFCCC and SEEA accounts can be enhanced by focusing the comparison on managed ecosystems only, see Fig. S7.

Fig. 7 shows how the net fluxes from the two frameworks agree. There is overall agreement between the UNFCCC and SEEA frameworks with their fluxes ($r = 0.47$) and their signs i.e., emission or sequestration, 58%. However, there are significant variation across countries (BRA: $r = 0.7$; PHL: $r = 0.13$) and flux categories (DEFORESTATION: $r = 0.6$; ORGANIC SOILS: $r = 0.16$). The remaining disagreement especially for organic soils and other land uses highlights systematic detection gaps, particularly for soil carbon and forest degradation. By contrast, UNFCCC reporting is limited to anthropogenic degradation activities within managed lands, thereby excluding many of the processes captured in the EO-based estimates.

## 4. Discussion

### EO products validation

The validation results of the land cover maps indicate that Earth Observation (EO) data showed good agreement with reference data (52.6–85.6%) especially in ecosystems that are more homogeneous in species composition and structure, such as coniferous and broadleaved forests. Some types, such as mangroves and wetlands, have limited reference data available, while certain ecosystems lack reference data entirely. As such, it is essential to collate and establish a consistent, standardized reference data set for ecosystems aligned with the System of Environmental-Economic Accounting (SEEA) accounting typology. The validation of the forest management map showed an accuracy ranging from 67.3–92.4%, which may be relatively higher than the rest of the EO data validation results, as it involved only two classes. The above-ground carbon (AGC) layer showed reasonable agreement with the reference data, although systematic deviations are also observed, see Figure S1. Biomass maps may inherit multiple bias sources from plot measurement and allometry choices to EO signal limits e.g., saturation at high biomass (Rodríguez-Veiga et al., 2016; Réjou-Méchain et al., 2019).

### Carbon accounts compilation based on earth observation

The key observation in Table 3 was the changes mainly driven by the dominant ecosystems in the countries which are broadleaved forests and coniferous forests in the examined countries in the tropics and non-tropics, respectively. The case of carbon emissions in Brazil is supported by the literature. Qin et al. (2021) and Fawcett et al. (2022) for instance conducted a regional analysis of carbon emissions in the Amazon which
<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Annual average net fluxes for 1-year, 5-year and 10-year accounting periods showing results of both accounting frameworks. Note that 1-year = inter-annual mean (SD = inter-annual SD); 5-year = mean of two 5-year block means (SD = between-block SD); 10-year = mean across 2010–2019 and 2010–2021 (SD = interannual SD). All summaries use the same year window.</figcaption></figure>

attributed emissions to both to deforestation and forest degradation. On the other hand, our findings in the USA are more difficult to compare due to limited similar studies that investigated carbon fluxes in the past decade in a carbon accounting context. Two global studies (Harris et al., 2021; Xu et al., 2021) and one national study (Yu et al., 2022) reported that the country served as a carbon sink from 2005 to 2017. Other countries that we found to act as carbon sinks such as Sweden at least 2017–2020 seem to be consistent with other recent studies that are also based on EO (Santoro et al., 2022) and for 2010–2017 for the Netherlands (Lof et al., 2017).

Shown in Table S3 are the extended carbon accounts and the table highlighted the possibility of depending on EO datasets to distinguish between emissions and sequestrations as well as the emissions related to deforestation and degradation. The net emissions attributed to degradation were found to be extremely low (1:10) compared to deforestation emissions. Recent studies also expressed that degradation is equally an emitter as deforestation (Lapola et al., 2023). In Brazil, for instance, forest degradation is caused by timber extraction from selective logging and habitat fragmentation, fires and drought (Csillik et al., 2024). Detecting forest degradation can be enhanced by increasing the spatial resolution of the ecosystem extent. A higher resolution map such as ≤ 10 m may be able to detect selective logging (Hethcoat et al., 2021). Aside from increasing spatial resolution of the ecosystem extent, forest degradation emissions can also be captured using time series biomass inputs. Forest degradation is likely to be underestimated when ecosystem extent inputs are coarse. This can be alleviated with the advent of high-resolution (10 m) annual land use land cover (LULC) maps, which, when complemented by coarser datasets like Climate Change Initiative (CCI) (300 m) that provide over 28 classes, allow for ecosystems consistent with SEEA typology.

Our results accord with recent syntheses that EO can support SEEA accounts when coupled to clear class typologies and quality indicators (Kokkoris et al., 2024). In line with Keith et al. (2021) and Melo et al. (2023), we frame EO as complementary to national data to reconcile observed discrepancies and become diagnostic rather than contradictory.

The EO-based carbon accounting has allowed annual carbon accounting, shortening the interval of account compilation especially

when compared with the country accounts based on national land cover data i.e., accounts spanning 5 or 10-year accounting periods due to dependency on the national land cover dataset updates (Hein et al., 2020). Here, the four consecutive annual accounting periods revealed that carbon fluxes can have fluctuations from net emissions to net sinks. In cases where accounts showed an abrupt increase in sequestration i.e., in the 2018–2019, 2019–2020 Sweden accounts, the EO data used for the accounts is likely uncertain given that small changes in biomass can be challenging to remotely sense i.e., due to low signal:noise (Santoro et al., 2022).

### The carbon pools of the six case countries

The EO-based accounts revealed the breakdown of carbon stocks and flows for each carbon pool. We learned that several countries had relatively higher carbon composition in the soils than in other pools particularly for the Netherlands, Mozambique, Sweden and USA. A generic explanation for this is the geographic and ecological regions (biomes) where these countries are located. It was clear in the global soil carbon assessment of Stockmann et al. (2015) that biomes with highest SOC were the tropical and subtropical moist broadleaf forests (Brazil and the Philippines), boreal forests (Sweden) and temperate conifer forests (Sweden and the Netherlands) and tropical savannah (Mozambique) are considered the fourth highest SOC region. Among the countries, we found Sweden to have the highest emissions from soils and also above-ground carbon among the country carbon pools. The decline and degradation of SOC are highly vulnerable to the compound- ing effects of land conversion and management as well as an increase in temperature from climate change effects (Beillouin et al., 2023). Another reason for the high SOC fluxes in the 2017–2019 periods particularly for non-tropical countries is the dynamic SOC dataset. The dataset incorporates annual forest loss dataset which is also reported to potentially overestimate losses over the said period (Palahí et al., 2021).

### Effects of accounting period intervals

The length of accounting periods strongly influenced estimates of net carbon fluxes and their variability. Our findings showed that longer
## Correlation (r) between This Study and UNFCCC

<figure><img src="image_11.png" /></figure>

## Agreement (%) between This Study and UNFCCC

<figure><img src="image_12.png" /><figcaption>**Fig. 7.** Correlation between accounting period and net fluxes (net emission or net sequestration) for all countries.</figcaption></figure>

intervals such as 5 years tend to smooth inter-annual variability, especially the fluxes of dominant ecosystems (Figure S5), potentially obscuring short-term dynamics critical for policy and management (Grassi et al., 2022). In contrast, annual estimates capture transient changes but exhibit higher variability, reflecting ecosystem responses to disturbances and land use change (Harris et al., 2021). Previous work also confirms that aggregating over longer intervals can reduce noise and increase robustness in national reporting, as stock-change estimates over decadal periods tend to dampen inter-annual variability (IPCC, 2008; Smith and Heath, 2016). However, this stability comes at the expense of missing short-term disturbance-driven fluxes that may be policy-relevant. Averaging maps (e.g., 2019–2021 to represent the 2020 epoch) may therefore be a compromise to minimize noise while retaining sensitivity to near-term changes (Araza et al., 2023).

## Comparison between carbon accounts of UNFCCC and this study

Comparison of the EO-based carbon accounts with country reports submitted to the UNFCCC shows a moderate level of agreement ($r$ = 0.47), with 58% consistency in the sign of net fluxes (sequestration versus emission). This level of agreement is influenced by the fact that country-reported estimates are primarily derived from National Forest Inventories (NFIs) and land cover maps, which are updated infrequently. Moreover, only carbon removals associated with specific human activities in managed lands are reported to the UNFCCC (Naburs et al., 2023). These results in potentially misleading accounts wherein countries report stability in the carbon stocks hence resulting in many “0” in the table (Table S4). This further reflects the fact
that UNFCCC reports have rarely depend on Earth Observation data for carbon estimation (Bastos et al., 2022; Melo et al., 2023). We also observed that the alignment between our EO-derived analysis and the UNFCCC reported data is much higher for countries with better forest data, i.e., Sweden, USA, Netherlands and Brazil (>63%). Here we further revealed how changes in managed forests can be significantly higher than what is reported to UNFCCC, see “DEFORESTATION” and “FORESTLAND” rows of Table 4. Another underestimated emission by the EO-based approach was peat emissions as observed in the reported soil emissions by the three non-tropical countries in Table 4. Another key source of discrepancy was the definition of forest degradation. While our EO-based approach interprets degradation broadly as any net carbon emission within forests remaining forests (consistent with SEEA), UNFCCC reports restrict it to human-induced processes in managed lands, leading to minimal reported values and underestimation of losses from natural disturbances or sub-canopy processes. Lastly, we treated emissions and sequestration (and successive periods) as independent, but in reality they can be correlated—within-year (between emission and uptake pathways) and across years (temporal autocorrelation). Ignoring these covariances can affect the confidence interval (CIs) and future assessments should model a joint covariance structure (e.g., multivariate and temporal error models) to propagate co-dependencies explicitly.

It should be noted that the estimates in this study both included managed and unmanaged forests and information from managed forests was only used for the comparison. The estimated proportion of managed forests used in this study are as follows: Brazil = 28%, Mozambique = 85%, the Netherlands = 100%, the Philippines = 52%, Sweden = 84% and USA = 90%, all based on the only high-resolution forest management dataset available (Lesiv et al., 2022). Our delineation of managed and unmanaged forests may differ from those used by countries in UNFCCC reporting. However, these should eventually be compared with EO-based estimates of managed and unmanaged forest proportions as they become available in future reports. Transparency in reporting carbon information from managed lands and in the assumptions used for defining managed forests should be a requirement for carbon reporting systems (Heinrich et al., 2023). With the transition to reporting under the Paris Agreement rules, it is expected this information is delivered by countries in the common reporting tables. The differing forest definitions and areas across frameworks help explain contrasting carbon flows. As shown in Fig. S3, even official sources such as UNFCCC and Forest Resource Assessment report notably different forest area. Along with the EO-based estimates (this study), the three forest areas all differ depending on the country. This reiterates the importance of defining terminologies related to carbon fluxes (Pongratz et al., 2014) especially when comparing between accounting frameworks. Nevertheless, there were accounts in this study that needed further investigation, particularly the relatively high emissions in USA and Sweden that may be caused by the forest loss EO data used as also reported in Palahí et al. (2021) and even the biomass map inputs (Hunka et al., 2023). Other similar studies have reported Sweden and USA as carbon sinks rather than sources (Santoro et al., 2022; Yu et al., 2022; Hurtt et al., 2024). There were also extreme natural hazards in the areas such as heatwaves that allowed significant forest degradation (Salomón et al., 2022). For instance, recent wildfires in the USA, likely linked to climate change, cause large carbon losses. UNFCCC inventories often exclude these as natural disturbances, whereas EO-based estimates (and SEEA-EA accounts) still capture them.

Outlooks in EO-based SEEA carbon accounting

There are several considerations and upcoming developments when using EO data for SEEA carbon accounting. The accuracy of biomass and land cover datasets themselves are increasing i.e., CCI version 6 which includes 2007, 2015 and 2021–2022 has just been released (as of writing, July 1, 2025). This not only enables the increase of

accounting periods but also the potential for attributing carbon fluxes to forest degradation using time series biomass data and analysis. The estimation of emissions from degradation should be complemented by using higher resolution ecosystem extent maps e.g., coming from recent land cover datasets derived from Sentinel-2 (10 m). They can be the basis for resampling and upscaling the CCI Land Cover 300 m dataset. Moreover, the use of national or regional land cover datasets such as the mapBiomas for Brazil, CORINE for Europe and NLCD data for USA can be explored. The challenge here is the harmonization of land cover classes for consistency with SEEA typology. The carbon accounting could also be formatted based on forest classes. An advantage of this is the comparability of the accounts to another international standard based on the Intergovernmental Panel on Climate Change (IPCC) guideline (Hunka et al., 2024). Lastly, recently published carbon stocks for specific ecosystems can be integrated e.g. mangrove SOC (Maxwell et al., 2023) and SOC changes from 1986–2022 using EO (Broeg et al., 2024) and relatively high-resolution 1993–2020 biomass (Liu et al., 2025).

Existing ecosystem accounting tools are also aimed at increasing use cases. For instance, the OpenEarthMonitor Project https://earthmonitor.org/ develops such as a tool using open-source frameworks (Li, 2020). Similarly, new online dashboards like the LULUCF Data Hub (https://forest-observatory.ec.europa.eu/carbon/fluxes) provide a means for transparent benchmark and comparison exercises for carbon accounting frameworks, while validation and inter-comparison platforms like the Multi-Mission Algorithm Platform (MAAP) can also help especially in sensitivity analysis and harmonization of different definitions of forests, forest management and so on (Albinet et al., 2019). These can allow accounting at different scales (e.g., sub-national) for countries with limited reference data available and would boost more demonstrations to growing studies exemplifying EO for ecosystem accounting (EA) (Kokkoris et al., 2024). Moreover, our reproducible workflow highlights how identified limitations are largely input-driven and can be progressively reduced as higher-quality EO or national datasets become available, similar to how EO-based carbon flux products have evolved through continuous updates and improvements e.g., Harris et al. (2021) and Gibbs et al. (2025).

We focused on the demonstration of using EO datasets to compile the accounts and the comparison with the UNFCCC accounts. The complexity of estimating such uncertainties including the assessment of cross correlation between emission and sequestration errors and the integration of ecosystem extent uncertainties (Venter et al., 2024) is already a stand-alone future study.

This study allows SEEA-aligned carbon accounts that distinguish between managed vs. all-ecosystems and deforestation–degradation, and we showed period-flexible EO-based accounts that scale to annual updates. Looking ahead, a modular EO and SEEA pipelines treating EO datasets as complementary to national reporting should support side-by-side (confluence-of-evidence) comparisons of ecosystem- and activity-based accounts, with uncertainty reported in a standardized way.

5. Conclusion

This study demonstrates that Earth observation (EO) data can be used to compile SEEA-aligned carbon accounts that are flexible enough to disaggregate carbon flows and distinguish between net fluxes from regrowth, deforestation and land cover change. The EO-based framework enables annual accounting, revealing short-term dynamics often obscured in multi-year national reporting cycles. However, further investigation is required for specific case countries and certain carbon pools, particularly soil organic carbon.

EO data provide a valuable complement to national greenhouse gas reporting, but improved methodological alignment is needed to enhance comparability between frameworks. Key limitations arise from the use of coarse ecosystem extent data, such as the 300 m CCI land
cover product, which constrains the detection of forest degradation. The integration of finer-resolution land cover datasets, including national products (e.g., mapBiomass for Brazil) and updated CCI versions, can improve attribution of carbon fluxes. Clearer and more consistent definitions of managed and unmanaged forests, together with systematic uncertainty assessment, remain essential. Initiatives such as the OpenEarthMonitor https://earthmonitor.org/ support ongoing alignment and consistency by providing harmonized tools and datasets for transparent, comparable ecosystem accounting.

### CRediT authorship contribution statement

**Arnan Araza:** Writing – original draft, Software, Methodology, Formal analysis, Data curation, Conceptualization. **Lars Hein:** Writing – review & editing, Supervision, Methodology, Formal analysis. **Yu Feng:** Writing – review & editing, Investigation, Data curation. **Joana Melo:** Writing – review & editing, Formal analysis, Data curation. **Martin Herold:** Writing – review & editing, Supervision, Funding acquisition, Formal analysis.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: AA reports financial support and article publishing charges were provided by Wageningen University & Research. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This study was supported by the Open Earth Monitor Project funded by the European Union (grant agreement No. 101059548); and CCI Biomass (4000123662/18/I-NB). This paper was produced with the financial support of the Prince Albert II of Monaco Foundation (FPA2) and the IPCC. The contents of this document are solely the liability of AA and under no circumstances may be considered as a reflection of the position of FPA2 and/or the IPCC. AA would like to acknowledge the Balik Scientist Program of DOST Philippines.

## Appendix A. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.181189.

### Data availability

Information and code about the target interactive web dashboard of the carbon pools can be seen here: https://github.com/arnanaraza/cshiny.

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