# IB-AGC: Annual 25 km global live biomass carbon product from SMOS L-band passive microwave vegetation optical depth

Xiaojun Li, Philippe Ciais, Frédéric Frappart, Klaus Scipal, Lei Fan, Hui Yang, Clément Albergel, Xiangzhuo Liu, Yuqing Liu, Mengjia Wang, et al.

<figure><img src="image_1.png" /></figure>

► To cite this version:

Xiaojun Li, Philippe Ciais, Frédéric Frappart, Klaus Scipal, Lei Fan, et al. IB-AGC: Annual 25 km global live biomass carbon product from SMOS L-band passive microwave vegetation optical depth. Scientific Data, 2025, 12 (1), pp.1156. 10.1038/s41597-025-05470-2. hal-05533187

HAL Id: hal-05533187

https://hal.inrae.fr/hal-05533187v1

Submitted on 2 Mar 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
scientific **data**

<figure><img src="image_3.png" /></figure>

Check for updates

OPEN
DATA DESCRIPTOR

# IB-AGC: Annual 25 km global live biomass carbon product from SMOS L-band passive microwave vegetation optical depth

Xiaojun Li<fnref n="1" />,², Philippe Ciais<fnref n="1" />,³, Frédéric Frappart<fnref n="1" />, Klaus Scipal⁴, Lei Fan⁵, Hui Yang⁶, Clément Albergel⁷, Xiangzhuo Liu<fnref n="1" />, Yuqing Liu<fnref n="1" />, Mengjia Wang⁸, Huan Wang⁶, Zanpin Xing⁹, Aurelien De Truchis¹⁰ & Jean-Pierre Wigneron<fnref n="1" />

Monitoring aboveground biomass carbon (AGC) stocks and their changes is crucial for understanding the global carbon cycle and the impact of climate change. Among remotely sensed methods, the use of the L-band (1.4 GHz) vegetation optical depth (L-VOD) derived from passive microwave satellite observations, offers rapid updates for timely monitoring of interannual AGC changes. L-VOD is sensitive to changes in the total water content of vegetation, which is determined by both AGC (the biomass of vegetation) and vegetation moisture content. While several methods have been used to understand and correct for the influence of the latter parameter when inferring AGC from L-VOD, there is still a lack of quantified corrections for its impact. Moreover, varying benchmark biomass datasets and fitting functions are currently used for converting L-VOD to AGC, making it difficult to harmonize or compare AGC estimates at regional and global scales. To address these issues, we first corrected the L-VOD time series for changes in the vegetation moisture content and then implemented a systematic global-scale calibration, resulting in annual AGC data set (called INRAE-BORDEAUX AGC, hereafter IB AGC) from 2010 to 2020 at a 25 km resolution. The accuracy assessments showed that IB AGC had a reasonably good spatial agreement with LiDAR referenced AGC data (R² = 0.60). Moreover, when aggregated at the national level, IB AGC exhibited stronger consistency with long-term net changes from country-level forest inventory data (R² = 0.62) than other mainstream satellite products. It is expected that IB AGC will provide an independent means for better monitoring the global vegetation carbon stocks and their variability in response to climate change.

## Background & Summary

Above-ground biomass carbon (AGC) in live vegetation is a main land carbon stock and can increase or decrease from environmental changes and direct human activities such as deforestation or forest management<fnref n="1" />,². The carrier of AGC, above-ground biomass (AGB), has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS)³, serving as a critical input for constraining Earth system models⁴,⁵ and a requisite for countries in their mandated carbon accounting⁶⁻⁸. Most of the Earth biomass is in forests, with large regional differences of carbon stocks and stocks density per unit area. Accurately monitoring changes

<footnote n="1">¹INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave-d'Ornon, France. ²Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, 611756, China. ³Laboratoire des Sciences du Climat et de l'Environnement, UMR 1572 CEA-CNRS-5 UVSQ, Gif-sur-Yvette, France. ⁴European Space Agency (ESA), Frascati, Italy. ⁵Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China. ⁶Institute of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, 100871, China. ⁷European Space Agency Climate Office, ECSAT, Didcot, UK. ⁸School of Geoscience and Technology, Zhengzhou University, Zhengzhou, 450001, China. ⁹Center for the Pan-Third Pole Environment, Lanzhou University, Lanzhou, 730000, China. ¹⁰Kayrros SAS, Paris, 75009, France. e-mail: Jean-Pierre.Wigneron@inrae.fr</footnote>
in AGC (ΔAGC) over time is crucial for enhancing our understanding and prediction of the effects of ongoing and future climate change, as well as the direct impacts of human activities on ecosystems, a key consideration for land-based climate change mitigation policies⁹.

Numerous studies have been conducted to estimate global and regional AGC and ΔAGC using various approaches⁷,¹⁰⁻¹³. National forest inventory (NFI) data, based on numerous forest plots measurements, are the most accurate resource for AGC estimation at local scale, but upscaling to a large region or a country requires dense and representative sampling, which is often lacking. NFI data are limited to forests, lack coverage of unmanaged forest lands, and do not provide comprehensive estimates of the fine-scale spatial distribution of AGC due to sparse, unevenly distributed sampling. Moreover, the common practice of conducting inventory revisits on multi-year cycles (e.g., in most countries, revisiting all NFI plots typically takes five years) in the 'stock change' approach⁶, along with reliance on simplified approaches for estimating forest growth rates and carbon removals from natural disturbances and harvest in the so called 'gain-loss' approach (e.g., assuming fixed forest growth rates), may not timely capture the impacts of climate induced disturbances and human intervention events on ΔAGC¹⁴,¹⁵. Additionally, the lack of standardized inventory strategies across countries and the lack of full transparency/availability of data and estimation methods to reproduce inventory estimates, hinders a spatially consistent estimation of AGC, despite considerable efforts by the United Nations Framework Convention on Climate Change (UNFCCC) to facilitate and homogenize the reporting of national greenhouse gas emission inventories (NGHGIs)¹⁶,¹⁷.

Alternatively, multiple satellite remote sensing technologies, such as light detection and ranging (LIDAR), optical, and active and passive microwave observations, are utilized to jointly map AGC or its corresponding AGB over large regions. The European Space Agency (ESA) Climate Change Initiative (CCI) biomass project produced AGB maps to address the GCOS ECV requirement for the epochs 2010, 2015–2021 at a 100 m pixel resolution using multi-radar satellite observations, spaceborne LiDAR data and forest inventory plot data¹⁸. Xu *et al.* (2021) mapped annual biomass carbon changes at a 10 km resolution from 2000 to 2019 using a machine-learning model trained on plot data, with multimodal optical and high-frequency microwave satellite inputs¹³. However, due to delays in acquiring, processing, computing, and formatting some of the input data (e.g. during satellite updates or replacements), the use of various remote sensing technologies poses challenges in achieving consistent and continuous biomass carbon assessments, particularly in terms of timeliness. In addition, optical and high-frequency microwave may be subject to saturation effects in regions of high biomass density such as tropical wet forests and old temperate forests¹⁹.

Unlike the above methods, L-band (1.4 GHz) vegetation optical depth (L-VOD), which is derived only from passive microwave satellite data, offers the advantage of rapid updates¹⁹,²⁰. Moreover, compared to optical satellite-based AGB proxies, such as the normalized difference vegetation index (NDVI)²¹,²², L-VOD is more sensitive to the biomass of trunks, branches and leaves and does not saturate even in dense forests owing to the higher penetration of low-frequency L-band radiation within the tree canopy. This enables its extensive use in tracking interannual changes in AGC in the tropics²³⁻²⁷, Siberia²⁸ and North American boreal forests²⁹. A global-scale application has also shown the spatiotemporal patterns of total biomass carbon change⁹, computed as the sum of L-VOD-derived AGC and below-ground biomass carbon, the latter derived from a map of below-to-above ground biomass ratios³⁰. Previous studies on L-VOD based AGC changes used a “space-for-time” methodology, starting by calibrating annual dimensionless L-VOD to AGC (mass of carbon per unit area; MgC ha⁻¹). This approach leverages the spatial gradients of multiple existing AGB datasets, along with dynamic change information derived exclusively from L-VOD to track ΔAGC¹⁹.

However, since L-VOD primarily probes the total vegetation water content (VWC), which can be written as the product of vegetation biomass (AGB) by its relative water content (RWC). The diurnal L-VOD changes are primarily related to RWC changes, with lower values in the evening as plant water potentials decrease during the day to sustain transpiration. The daily and monthly L-VOD variations reflect intertwined changes in both AGB and RWC. For instance, Tian *et al.* (2018) showed that trees can store water during wet periods and use it weeks later to sustain transpiration³¹. This raised concerns about the direct use of annual L-VOD changes to accurately infer biomass changes³². Additionally, previous studies have used inconsistent benchmark AGC datasets or different fitting methods to calibrate L-VOD in various regions of interest, making it difficult to harmonize or compare AGC estimates at regional or global scales¹⁹. Given that L-VOD provides a crucial perspective for understanding ΔAGC, this study aims to address these issues by proposing a correction for changing water content in the L-VOD time series and subsequently adopting a more systematic and improved global-scale L-VOD calibration approach based on Yang *et al.*⁹ (Fig. 1). Finally, based on these corrections and improvements, annual AGC stock data (referred to as INRAE-BORDEAUX AGC, or IB AGC) for the period from 2010 to 2020, with a spatial resolution of 25 km, is generated.

## Methods

In this study, the IB daily L-VOD data from 2010 to 2020 was computed based on the SMOS-IC algorithm implemented on ESA's Soil Moisture and Ocean Salinity (SMOS) observations, which is the first L-band passive satellite mission launched in 2010²⁰,³³. IB SMOS-IC is the most recent SMOS product based on an optimized algorithm relying on the inversion of the L-band Microwave Emission of the Biosphere (L-MEB) model²⁰,³⁴. IB L-VOD is also the reference product for monitoring ΔAGC used in the ESA Climate Change Initiative Project CCI-RECCAP-2 (https://climate.esa.int/en/projects/reccap-2/). The comparable performance of IB SMOS-IC L-VOD, together with its Soil Moisture (SM) product, relative to other counterparts has been demonstrated in several evaluation studies²¹,³⁵⁻³⁸. There are several main steps involved in deriving IB AGC from SMOS-IC L-VOD, including low-quality daily L-VOD data filtering, RWC correction, calibration, and preliminary evaluation (Fig. 1). Details of the data sources used in these steps including root zone soil moisture (RZSM), benchmark AGC datasets, NFI and forest loss/gain are listed in Table 1.
<figure><img src="image_4.png" /><figcaption>Fig. 1 Workflow for estimating global-scale IB AGC from SMOS-IC L-VOD.</figcaption></figure>

**L-VOD filtering process.** The retrieval accuracy of L-VOD can vary significantly due to topography, frozen surface conditions (e.g., ice and snow), the proportion of water bodies within a 25 km grid cell, and radio frequency interference (RFI)²¹. Filtering out these potential spurious observations is crucial to ensure the reliability of AGC derived from L-VOD. In a previous study by Fan *et al.*²³, the root mean square error (RMSE) between simulated and measured brightness temperatures was used to filter L-VOD observations affected by RFI, while the scene identification flag (SF) was used to filter pixels contaminated by strong topography, water bodies, and frozen soil conditions. This simple method has proven effective in both tropical²⁵,²⁶ and Siberian regions²⁸,³⁹. Here, we enhanced this RFI filtering process by additionally considering inter-orbit mean differences in L-VOD (ΔL-VOD > 0.05) between ascending and descending orbits over a three-month time window⁴⁰, as well as outliers (i.e., data outside of mean ± 2*STD) (Fig. 1). A detailed description of this triple-filtering process and its effectiveness can be found in Yang *et al.*⁹. In this study, we used filtered L-VOD observations from the ascending orbit acquired at 6:00 am local time, as plants generally have higher water potential and content in the morning, making this orbit less sensitive to variations in relative water content due to water stress and more suitable for monitoring AGC than the descending orbit at 6:00 pm²⁵,⁴¹.

**L-VOD correction for RWC effects.** To correct for the potential influence of RWC variations on monthly L-VOD changes, we adopted a simple multiplicative model following previous studies⁴²⁻⁴⁴. The rationale for the model is that L-VOD is linearly proportional to VWC and thus scales with both dry biomass (AGB) and RWC as follows:

$$L\text{-VOD} = b \times \text{VWC} = b \times \text{AGB} \times \text{RWC} \quad (1)$$

where b is a proportionality constant dependent on canopy structure and electromagnetic frequency. At L-band, the variation of b over time is minimal and is thus often considered time-invariant⁴⁵. Seasonal fluctuations in AGB are primarily influenced by leaf growth and senescence, with stems and branches making comparatively smaller contributions⁴³,⁴⁶. Previous studies suggest that leaf biomass can be estimated by exponential or linear functions using NDVI⁴⁷⁻⁵⁰, while the RWC of plant components can be linearly related to their leaf water potential ($\psi_L$)³². Thus, Eq. (1) can be written as:

$$L\text{-VOD} = b \times \text{AGB} \times \text{RWC} = b \times (p \cdot \text{NDVI} + s) \times (m \cdot \psi_L + n) \quad (2)$$

where p, s, m, n are empirical parameters that vary spatially but not temporally. Note this model may not fully capture the growth of woody plant components over time in dense forests, as NDVI act primarily as proxies for leaf biomass and may become saturated for dense vegetation canopies. However, this simple model is not used to simulate biomass changes, but is mainly used to estimate the first order effects of RWC changes in the L-VOD time series, ultimately leading to a correction.

In this study, to remove the signal of varying RWC from L-VOD as accurately as possible, we built seven multiple regressions of L-VOD against NDVI and two commonly used $\psi_L$ proxies, namely normalized difference water index (NDWI) and root zone soil moisture (also called root zone soil water content, SWC), according to Eq. 2. The specific form of these regressions can be found in Supplementary Text 1. We chose NDWI because it is based on near-infrared and short-wave infrared wavelengths and is sensitive to changes in the liquid water content of vegetation canopies. Since variations in the whole plant water content are largely determined by foliage
<table><caption>Table 1. Data sources used in this study.</caption><thead><tr><td>Data source</td><td>Description</td><td>Spatial sampling</td><td>Time period</td><td>Use</td></tr></thead><tbody><tr><td>IB L-VOD</td><td>Low frequency L-VOD product derived from the SMOS satellite observations using the SMOS-IC algorithm in version 2<sup>20</sup></td><td>25 km</td><td>2010–2020</td><td>AGC Production</td></tr><tr><td>MODIS NDWI</td><td>Monthly NDWI calculated in Google Earth Engine based on MODIS surface reflectance product (MOD09A1 V6.1)<sup>51</sup></td><td>500 m</td><td>2010–2020</td><td>L-VOD RWC correction</td></tr><tr><td>MODIS NDVI</td><td>16-Day global MODIS Aqua NDVI data extracted from the MOD13C1 V6.1<sup>71</sup></td><td>0.05°</td><td>2010–2020</td><td>L-VOD RWC correction</td></tr><tr><td>ERA5 Soil moisture</td><td>Monthly root zone soil moisture (0–100 cm) from the European Centre for Medium Range Weather Forecasts Reanalysis version 5 (ERA5)<sup>72</sup></td><td>0.25°</td><td>2010–2020</td><td>L-VOD RWC correction</td></tr><tr><td>GEDI</td><td>Global Ecosystem Dynamics Investigation (GEDI) L4B Gridded Aboveground Biomass Density between 51.6° N and 51.6° S latitudes, Version 2<sup>73</sup></td><td>1 km</td><td>2020</td><td>Reference AGB map for calibration</td></tr><tr><td>Saatchi AGB</td><td>Global aboveground biomass map generated from multiple datasets including inventories, MODIS images and Quick Scatterometer (QuikSCAT) data<sup>65</sup></td><td>1 km</td><td>2015</td><td>Reference AGB map for calibration</td></tr><tr><td>GlobBiomass</td><td>Global remotely sensed map of aboveground biomass in living trees<sup>7</sup></td><td>100 m</td><td>2010</td><td>Reference AGB map for calibration</td></tr><tr><td>spawn</td><td>Harmonized global biomass carbon densities based on multiple existing biomass maps include both above- and below-ground components, covering woody and herbaceous vegetation types<sup>30</sup></td><td>300 m</td><td>2010</td><td>Reference AGB map for calibration</td></tr><tr><td>Avitabile</td><td>Pantropical aboveground biomass map generated by merging multiple datasets using machine learning methods<sup>66</sup></td><td>1 km</td><td>2016</td><td>Reference AGB map for calibration</td></tr><tr><td>Baccini</td><td>Pantropical aboveground biomass map generated using multiple datasets and random forest<sup>74</sup></td><td>500 m</td><td>circa 2007–2008</td><td>Reference AGB map for calibration</td></tr><tr><td>Airborne LiDAR-based AGB maps</td><td>Local AGB maps based on airborne LiDAR data from Araza et al.<sup>56</sup> and Rodda et al.<sup>57</sup></td><td>100 m</td><td>circa 2007–2020</td><td>Validation</td></tr><tr><td>FRA</td><td>National statistics on woody vegetation biomass carbon from the FAO Global Forest Resources Assessment (FRA)<sup>75</sup></td><td>National</td><td>2019</td><td>Validation</td></tr><tr><td>UNFCCC</td><td>National statistics on changes in total biomass submitted to the UNFCCC by Annex I countries<sup>76</sup></td><td>National</td><td>2010–2019</td><td>Validation</td></tr><tr><td>Forest area fraction</td><td>Annual evergreen forest maps in the Brazilian Amazon generated from time series MOD09A1 product<sup>25</sup></td><td>500 m</td><td>2010–2019</td><td>Comparison</td></tr><tr><td>GFW Forest loss and gain data</td><td>The global forest change product (v1.10) generated from Landsat imagery by Global Forest Watch (GFW)<sup>60</sup></td><td>30 m</td><td>2010–2019</td><td>Comparison</td></tr><tr><td>MODIS fire data</td><td>Annual Active Fire Maps integrated from MODIS Level 3 Thermal Anomalies/Fire 8-Day Global 1 km SIN Grid product (MOD14A2, V6.1)<sup>77</sup></td><td>1 km</td><td>2010–2019</td><td>Comparison</td></tr><tr><td>CCI</td><td>Global aboveground biomass maps generated by ESA' Climate Change Initiative (CCI) Biomass project for five epochs (2010, 2017, 2018, 2019 and 2020)<sup>18</sup></td><td>100 m</td><td>2010, 2019</td><td>Inter-comparison</td></tr><tr><td>Xu</td><td>Global vegetation live biomass carbon based on optical and high-frequency satellite observations using machine learning techniques<sup>13</sup></td><td>10 km</td><td>2010–2019</td><td>Inter-comparison</td></tr><tr><td>Non-intact forest</td><td>Non-intact "forest" masks improved based on Potapov et al. (2017), taken as a proxy for countries' managed forest<sup>17</sup></td><td>0.05°</td><td>2000–2013</td><td>Defining managed forests</td></tr></tbody></table>

storage, and stem water content covaries with leaf water content, NDWI serves as a reasonable proxy for $\psi_L$<sup>51</sup>. Additionally, as hydraulic transport follows a gradient of water potential from soil to leaf<sup>13,52</sup>, it is reasonable to assume that the possible interannual variability in RWC is related to $\psi_L$, soil water potential, and soil moisture. Note that the Live Fuel Moisture Content (LFMC) may also be a good indicator of vegetation RWC changes<sup>53</sup>. However, there is currently no global LFMC product with an update frequency consistent with L-VOD (once such a product becomes available, it can also be considered in the RWC corrections). To perform the regression, we first normalized the NDVI and $\psi_L$ proxy data, and then utilized a 3-by-3 pixels spatial moving windows to increase the modeling sample size for constructing Eq. 2. We then calculated the Akaike Information Criterion (AIC) for the seven models to evaluate how well each model, based on different proxy input combinations, explained the L-VOD observations. The best-fit model, according to AIC, is the one that explains the greatest amount of variation using the fewest possible independent variables<sup>54</sup>. Finally, the moisture content-induced L-VOD changes can be removed by subtracting the RWC-related terms according to the best model.

**Global AGC derived from L-VOD.** As in previous studies (Fan et al., 2019; Yang et al., 2023)<sup>9,23</sup>, the SMOS L-VOD was converted to AGC density using existing published biomass maps as calibration references. However, considering the true AGC is unknown and to avoid reliance on a single reference dataset, we collected several widely used static biomass maps and aggregated them to match the SMOS resolution (i.e., 25 km grid cells),
including four global maps (GEDI, limited to 51.6° N–S, ‘Satchi’, ‘GlobBiomass’ and ‘ Spawn’) and two pan-tropic maps (‘Avitabile’ and ‘Baccini’) (Table 1). Note that these reference datasets may not be fully independent. For example, while ‘ Spawn’ provides global estimates of both woody and herbaceous biomass, its woody biomass component is derived from ‘GlobBiomass’. Assuming that approximately 50% of aboveground biomass consists of carbon, these biomass data (Mg ha⁻¹) was converted to AGC (Mg C ha⁻¹) utilizing a conversion factor of 0.5⁵⁵. Similar to Yang et al.⁹, instead of creating a single global spatial calibration function, where possible outliers could affect the fitting parameters, we divided the world into three regions: the tropics (30° S–30° N), the Northern Hemisphere (30° N–90° N), and the Southern Hemisphere (90° S–30° S). We then used all previously published equations for the three regions⁹,¹¹,²⁸ and separately calculated the optimal coefficients for each calibration equation between the annual L-VOD and the reference AGC for each region.

$$AGC = a_1 \times \frac{\arctan(b_1 \times (L_{VOD} - c_1)) - \arctan(-b_1 \times c_1)}{\arctan(b_1 \times (Inf - c_1)) - \arctan(-b_1 \times c_1)} + d_1 \text{ if tropics} \quad (3)$$

$$AGC = a_2 + b_2/(1 + e^{-c_2(L_{VOD}-d_2)}) \text{ if extra-tropics} \quad (4)$$

$$AGC = a_3 \times L_{VOD}^{b_3} \text{ if extra-tropics} \quad (5)$$

where a₁, b₁, c₁, d₁, a₂, b₂, c₂, d₂, a₃ and b₃ are fitted coefficients. Here, we used the annual L-VOD from 2011 for calibration. Notably, the year of L-VOD used for the calibration has a very little impact on the final result as evidenced in Fan et al.²⁸. To construct a robust relationship, yearly 2011 L-VOD values were ranked and grouped into bins of 250 grid cells. For each bin, the mean AGC from the reference map was computed, resulting in an AGC–L-VOD curve. This binning approach helps to reduce the influence of noisy pixels in both the VOD data and the reference AGC¹¹. We conducted the calibrations of Eq. (3) using six AGC reference maps for the tropical region, tropical America, and tropical Africa, resulting in 18 calibration equations. Equations (4) and (5) were applied to the Northern and Southern Hemispheres, respectively, based on three global AGC reference maps, yielding 8 calibration equations for each hemisphere. In total, 34 sets of calibration parameters were obtained from this process (Table S1). Note that these calibration equations were used solely to define the spatial relationship between L-VOD and AGC and were applied to each annual L-VOD map. As a result, the temporal dynamics of AGC are entirely driven by L-VOD. For each year, the mean of the 34 calibrated maps was used to produce the final IB AGC map. There are two main sources of uncertainty associated with the L-VOD-derived live AGC estimates, including: i) external uncertainties, arising from systematic errors in the reference biomass maps; and ii) internal uncertainties, stemming from calibration procedures and sampling strategy limitations. Previous analyses²³,³⁹ have shown a strong dominance of external errors over internal uncertainties, with the difference reaching approximately an order of magnitude. In light of this, we quantified the calibration uncertainty using the standard deviation derived from 34 individual estimates (σcalibration), which primarily reflects external uncertainty resulting from systematic errors in the reference AGC maps, as done in Feng et al.²⁷. We then calculated total biomass carbon (BC), defined as the combined aboveground and belowground biomass carbon (BGC), using the global per-pixel ratio of BC to AGC provided by Spawn et al.³⁰. The uncertainty in BC (σBC) was calculated based on the law of error propagation as follows:

$$\sigma_{BC} = BC \sqrt{\left(\frac{\sigma_{calibration}}{AGC}\right)^2 + \left(\frac{\sigma_{ratio}}{ratio}\right)^2} \quad (6)$$

**Evaluation of IB AGC.** Wigneron et al. (2024) provided a detailed review of evaluation studies using L-VOD to estimate AGC¹⁹. Here, to further evaluate the IB L-VOD derived AGC, we collected independent reference data, including lidar-based AGC maps and NFI data. Spatially, we used local AGC maps based on airborne LiDAR data obtained from Azza et al.⁵⁶ and Rodda et al.⁵⁷, as they cover regions not represented in the plot inventory database and have been shown to provide valuable indications for validating biomass datasets at a spatial resolution of 25 km⁵⁶. We also compared the L-VOD derived AGC estimates with NFI data reported by the Food and Agriculture Organization of the United Nations (FAO) for individual countries in 2019. Temporally, we collected data on the net change in total biomass carbon in managed forests from 2010 to 2019, as reported to the UNFCCC by 43 Annex I countries (Table 1). These countries, considered advanced economies, have annual greenhouse gas reporting commitments under the UNFCCC¹⁷. Non-intact forest masks, refined from Potapov et al.¹⁷,⁵⁸, were used as a proxy for managed forest areas. Additionally, we gathered forest area fractions for the Brazilian Amazon and forest loss data from the Global Forest Watch (GFW) to evaluate whether IB AGC can capture significant forest area disturbances²⁵,⁵⁹,⁶⁰. We also compared the validation results of IB AGC with those obtained from two other datasets for the years 2010 to 2019, including the CCI AGC maps¹⁸ and the global annual biomass data produced by Xu et al.¹³, herein referred to as CCI and Xu. The CCI and Xu datasets have finer spatial resolutions than IB L-VOD, with the CCI product in particular providing biomass estimates at a 100 m resolution (Table 1). To address the spatial resolution mismatch, both datasets were aggregated to a common 25 km resolution using an average aggregation method. While a comprehensive analysis of scale effects is beyond the scope of this study, it should be noted that spatial resolution is an inherent consideration in AGC map assessments, as different applications require different levels of detail. For example, the CCI product has been evaluated across multiple spatial scales, from 100 m to 25 km, as detailed in Azza et al.⁵⁶.
<table><caption>Table 2. Description of gridded data layers for the IB AGC product.</caption><thead><tr><td>Data layer</td><td>Description</td><td>Units</td></tr></thead><tbody><tr><td>CRS</td><td>Coordinate reference systems (CRS) include spatial reference information and geographic transformation parameters.</td><td>/</td></tr><tr><td>Lat</td><td>The latitude of the center of each grid cell</td><td>Degree (°)</td></tr><tr><td>Lon</td><td>The longitude of the center of each grid cell</td><td>Degree (°)</td></tr><tr><td>Time</td><td>Year information starting from 2010</td><td>/</td></tr><tr><td>AGC</td><td>Annual aboveground biomass carbon density derived from L-VOD</td><td>MgC ha<sup>-1</sup></td></tr><tr><td>AGC_uncertainly</td><td>The uncertainty (standard error) of the annual aboveground biomass carbon density derived from L-VOD</td><td>MgC ha<sup>-1</sup></td></tr><tr><td>BC</td><td>Annual total biomass carbon density converted from L-VOD derived AGC</td><td>MgC ha<sup>-1</sup></td></tr><tr><td>BC_uncertainly</td><td>The cumulative uncertainty (standard deviation) of the annual total biomass carbon density converted from L-VOD derived AGC</td><td>MgC ha<sup>-1</sup></td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>Fig. 2 Global maps of IB AGC density (a) and its uncertainty (b) for 2020 at a spatial resolution of 25 km. Gray pixels indicate areas removed due to strong RFI and high open water fractions that affect L-VOD.</figcaption></figure>

## Data Records

The global annual 25 km IB AGC product derived from SMOS-IC L-VOD for the period 2010 to 2020 is openly available on Zenodo⁶¹ and will be regularly updated on the INRAE Bordeaux Remote Sensing Product website (https://ibremote-sensing.inrae.fr/). Note that the AGC derived from L-VOD represents aboveground live biomass across all vegetation types, including both woody and herbaceous components, since the 25 km L-VOD signal reflects a mixture of contributions from different vegetation types. The dataset is stored in netCDF4 format and projected on the global cylindrical Equal-Area Scalable Earth Grid version 2.0 (EASE-Grid 2.0), with dimensions of 584 by 1388 and a grid sampling resolution of 25 km³. The IB AGC density map and its associated uncertainty layer, representing the standard deviation of the calibration, are included in the layers (n = 8, Table 2) of the dataset. Figure 2 shows the global distribution of IB AGC and its associated uncertainty in 2020. External uncertainty, resulting from systematic errors in the reference AGC map, is particularly pronounced in Congo basin, where σ<sub>calibration</sub> can reach up to 20 MgC ha<sup>-1</sup>. Note that in addition to deriving AGC from L-VOD, we have provided a sub-product of total biomass carbon (BC) density according to the aforementioned ratio method. The associated uncertainty is also provided, representing the cumulative uncertainty originating from the AGC calibration and the ratio between AGC and BGC, which is calculated using Eq. 6.
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Spatial patterns of trends in L-VOD-derived AGC before (a) and after (b) correction of RWC, their differences (c) and their scatter plot (d) based on the NDWI × SWC model during 2010–2020. Note that the “MPD” in d represents the mean percentage difference before and after correction for RWC.</figcaption></figure>

## Technical Validation

**Correcting for the effects of RWC in L-VOD.** The model incorporating NDVI, NDWI and SWC, built according to Eq. 2, had the highest average coefficient of determination (R²), explaining approximately 35% of the variation in L-VOD (Fig. S1a). This was followed by models using combinations of two predictor variables, with R² values ranging from 0.26 to 0.28. Models that included one variable exhibited very limited predictive power for L-VOD globally, with the model including only SWC having the lowest R² value, ~ 0.17 on average. This result suggests that a relatively large proportion of L-VOD variation is not driven by changes in water content, as approximated by SWC. It was found that the differences in AIC among the seven models were small, with the models that included SWC × NDVI or SWC × NDWI having the smallest AIC value of ~291, indicating that these two models could best fit the L-VOD (Fig. S1b).

Considering that the SWC × NDWI model explained L-VOD better than using any of these water proxies alone (Fig. S1a) and maximized the simulation of changes in the vegetation moisture content signal, we used this model to remove the contribution of RWC from the L-VOD. It was found that the spatial distribution patterns of trends in L-VOD-derived AGC during 2010–2020 were generally consistent before and after correcting for RWC, indicating that the correction had minimal impact (see Fig. 3a,b). For instance, regions such as the eastern Amazon, the central Congo Basin, Australia, and the Siberian forest consistently exhibited distinct hotspots of AGC losses both before and after correction. Moreover, in absolute terms, the value of the trend change induced by correcting for RWC was small, with an overall mean percentage difference of 6.08%, and on a pixel-by-pixel basis, the change did not exceed 0.2 MgC ha⁻¹ yr⁻¹ in most regions of the globe (Fig. 3c). In particular, this correction did not affect the direction of the trend (Fig. 3d), but it reduced the magnitude of the trend values. For example, the decreasing trend over Australia was less pronounced after correcting for RWC (Fig. 3a,b). This is in line with the results obtained in tropical intact forests using only one of the two vegetation moisture content proxies⁴⁴.

**Comparison with Lidar-based AGC and NFI data.** Figure 4 shows a comparison between AGC density estimates from L-VOD and LiDAR-based reference data at the 25 km grid scale. Note that the LiDAR reference data were subject to strict quality control and included uncertainty estimates, as detailed in Araza *et al.*⁵⁶. During aggregation to 25 km grid cells, these uncertainties were used as weights, and only grid cells meeting a minimum number of reference observations were retained. It was found that AGC density estimates based on IB L-VOD showed reasonably good spatial agreement with the LiDAR reference data, comparable to the agreement observed with the other two remote sensing products, with a coefficient of determination (R²) of about 0.6 and IB L-VOD exhibited the lowest relative RMSE (45.71%). It tended to underestimate the LiDAR-derived local AGC reference
<figure><img src="image_7.png" /></figure>

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

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

Fig. 4 Comparison of remotely sensed (RS) AGC estimates with airborne LiDAR based local AGC maps, for (a) IB L-VOD, (b) CCI, (c) Xu, and (d) corresponds to the distribution of LiDAR reference data. RRMSE represents the relative root mean square error between the estimated and observed AGC, normalized by the mean of the estimated AGC values.

data, with a bias of -5.92 MgC ha⁻¹. This underestimation was particularly evident in regions with high biomass density, where AGC exceeded 140 MgC ha⁻¹. In contrast, Xu overestimated the LiDAR reference data, whereas CCI exhibited the smallest bias. This may be attributed to the finer native spatial resolution of the latter, in comparison to the coarser resolutions (≥10 km) of both the L-VOD and Xu data sets. Similar patterns were also observed when the three remotely sensed biomass products were spatially aggregated at the country level and compared with total live woody biomass reported in the FAO Global FRA (Fig. S2). In line with Grassi et al.¹⁷, this process applied a forest definition of >30% tree cover based on the tree cover dataset provided by Hansen et al.⁶⁰ from around 2010. In particular, the biomass density estimated by CCI was closer to the FAO-reported values in Annex I countries, most of which have relatively dense and reliable NFI, except for Russia. These results suggest that using CCI AGC to calibrate L-VOD has potential for future improvements.

The above comparison analysis was 'static' and did not consider changes in AGC. Evaluating remotely sensed datasets that monitor changes in AGC is very challenging due to the lack of reference data, as showed by Wigneron et al.¹⁹. Note that although forest inventories are independent from satellite-based products and provide accurate estimates, they are difficult to use for assessing the interannual variability of IB L-VOD derived AGC. An evaluation of AGC changes (ΔAGC) estimated by four different satellite maps across multiple spatial scales found that comparisons with NFI data only showed some agreements at aggregation levels below 10 km⁵⁶. To this end, we first compared the three remotely sensed ΔAGC products with local ΔAGC maps from airborne LiDAR (Fig. S3). All three products showed weak correlations (R² < 0.12), and the magnitude of both estimated losses and gains was smaller than that obtained from the LiDAR ΔAGC, consistent with the findings of Arrza et al.⁵⁶. In addition, we assessed the long-term net total carbon change estimated by IB L-VOD with the country-level NFI reported by the land use, land-use change, and forestry (LULUCF) sector under the UNFCCC for each Annex-I nation from 2010 to 2019 (Fig. 5). LULUCF focus on the impact of human activities on forest carbon pools or fluxes⁶, requiring Annex-I countries to submit regular and continuous data on live biomass. However, this reporting is only made for managed forests and the delineation of such forests is not openly accessible, although most countries, like those in Europe, consider all forests as managed. To ensure a fair comparison, we used the intact forest boundaries as a proxy for managed forest boundaries to exclude unmanaged forests from the aggregation statistics of satellite-estimated biomass changes¹⁷.
<figure><img src="image_11.png" /><figcaption>Fig. 5 Comparison of biomass change (Δbiomass) estimates between RS-based products and LULUCF reported values during 2010-2019 over managed forests of Annex-I countries⁷⁶, for (a) IB L-VOD, (b) CCI, (c) Xu, and (d) corresponds to the distribution of Annex I parties. The bar chart in Fig. 5d represents biomass carbon changes in Annex I countries in the Northern Hemisphere, estimated by different methods. Note that caution is needed when interpreting the CCI results, as significant uncertainties may arise when comparing the product for different years due to the use of different sensor sources, particularly for 2010 and the years after 2017, as acknowledged in the CCI user guide¹⁸.</figcaption></figure>

Both satellite estimates and LULUCF reports indicated that the total biomass change during the 2010s was largest in four countries, including Russia, the United States, Canada, and Australia (Fig. 5a,b,c). However, significant discrepancies were found among the different satellite products in terms of both direction and magnitude. For instance, LULUCF, CCI and IB L-VOD estimated a net increase in forest biomass in Canada during the 2010s, whereas Xu estimated a net decrease. In addition, the IB L-VOD estimates indicated that biomass carbon in Annex-I countries in the Northern Hemisphere (Fig. 5d; excluding Australia) increased at a rate of 0.40 PgC·yr⁻¹ during the 2010s, lower than the LULUCF reported rate of 0.58 PgC·yr⁻¹. This underestimation could be partly due to the NFI providing more information on local biomass carbon changes in intact, mature, or old-growth forests and neglecting carbon losses in surrounding areas caused by deforestation and degradation. Nonetheless, the CCI (with large negative changes in the USA) and Xu (with large negative changes in the USA and Canada) products showed opposite biomass carbon changes, both indicating a net decrease with values < −0.1 PgC·yr⁻¹. These discrepancies could also be partly explained by the fact that the use of intact forest boundaries as a proxy for managing forests boundaries may differ from what is actually used in different countries. Nevertheless, the biomass carbon changes in managed forests estimated by IB L-VOD showed a higher spatial correlation (R² = 0.60, P < 0.01) with NFI data from LULUCF across Annex-I countries than the other two products (R² = 0.01, P = 0.56 for CCI; R² = 0.08, P = 0.13 for Xu) (Fig. 5a–c).

**Comparison of responsiveness to forest disturbances.** We then compared the satellite estimates of biomass changes with forest area disturbance events. This indirect comparison allowed us to assess whether remotely sensed biomass data could detect signs of forest disturbances, as such disturbances typically cause forest degradation or tree mortality, which hindered the accumulation of live biomass carbon. We found that the spatial distribution of live biomass losses from IB L-VOD (Fig. 6a) generally corresponded well to the global forest area losses reported by the Global Forest Watch (GFW)⁶⁰. Specifically, in areas where GFW reported high proportions of forest area loss in the eastern Amazon, the central Congo Basin, and some Siberian forests, IB L-VOD identified hotspots of biomass losses (Fig. 6a,d). The IB L-VOD loss patterns collocated with the GFW products⁶⁰
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

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

<figure><img src="image_15.png" /><figcaption>**Fig. 6** Spatial patterns of changes in forest biomass carbon density and forest loss fraction during 2010-2019. (a–c), net biomass changes in (a) IB L-VOD, (b) CCI and (c) Xu. (d) Forest loss fraction, calculated as the proportion of the summed area of forest loss at 30 m spatial resolution reported by Global Forest Watch (GFW)⁶⁰ based on Landsat imagery within each 25 km grid cell.</figcaption></figure>

(Fig. 6d) were generally well reproduced by CCI, whereas the Xu did not detect biomass carbon loss effectively in some deforestation hotspots (e.g., the eastern Amazon)⁶². Instead, this dataset primarily showed greening trends along the deforestation arc.

We then examined the quantitative relationship between total losses of live biomass and forest area losses across the subregions defined in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) (Fig. S4). Spatially, the relationship between forest loss area and biomass carbon loss revealed by the IB L-VOD dataset was similar to those from the CCI and Xu datasets, all following a nonlinear pattern. While all three quadratic fitting curves showed similar explanatory power of forest loss for biomass carbon loss ($R^2$ ~ 0.53–0.57), IB L-VOD and CCI exhibited steeper declines in biomass carbon loss, whereas Xu showed a more gradual decrease (Fig. 7c). For example, in the 2010s, GFW reported that eastern Siberia (ESB) had the largest forest loss area of 4.96 × 107 ha among all IPCC AR6 subregions, with total biomass carbon loss estimated at −1.01 ± 0.13 Pg C and −1.10 Pg C by L-VOD and CCI, respectively, while the Xu produced a lower estimate of −0.49 Pg C. Given the large discrepancies among the three satellite products in northern regions (Fig. 5), and that wildfire has been reported as a major driver of forest loss in this area (Fig. S5)²⁸,⁶², we focused on IPCC AR6 subregions north of 30°N to examine how biomass carbon loss varied with area burned by fire (Fig. 7d-f). Similarly, both IB L-VOD ($R^2$ = 0.65) and CCI ($R^2$ = 0.85) showed a sharp negative relationship between the extent of biomass losses and total burned area, while Xu showed a more gradual response with a relatively low $R^2$ value of 0.44. This might be because optical observations on which Xu is based are more sensitive to post-fire greening of herbaceous and shrub vegetation, which tends to recover faster than aboveground live biomass after fire disturbance, as shown in previous studies¹⁹,²⁸,⁶³. However, Xu benefits from a longer time span, which is valuable for capturing long-term post-disturbance trends.

In contrast to biomass loss, the relationship between total biomass gains and forest areas gain was relatively weak across all three datasets (Fig. 8a–c). Specifically, both IB L-VOD and CCI showed moderate fits ($R^2$ = 0.42 and $R^2$ = 0.44, respectively), with clear nonlinear increases in biomass accumulation as forest gain area increased. The Xu dataset, however, exhibited a lower explanatory power ($R^2$ = 0.25) and a relatively flatter curve. Nevertheless, this result should be interpreted with caution, as the observed mismatch reflects the limitations of both global forest area gain and satellite-derived biomass datasets. For example, Landsat imagery has limited capacity to accurately detect the year of canopy closure and to distinguish between natural forest regrowth, plantations, and woody encroachment outside traditional forest domains⁶⁴. In addition, the forest gain product is a binary indicator, assigning either 1 (gain) or 0 (no gain). Due to the lack of annual forest gain data, we assumed that gains occurred uniformly over time during the study period. While for satellite biomass
<figure><img src="image_16.png" /><figcaption>**Fig. 7** Relationship between total biomass carbon (BC) loss estimated by three satellite products and forest loss (top panel) and burned area (below panel) across IPCC AR6-defined subregions (Fig. S4) during 2010–2019. Results are shown for (a,d) IB L-VOD, (b,e) CCI, and (c,f) Xu. Note that for burned area, only subregions located north of 30°N are included in the analysis.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>**Fig. 8** Total biomass carbon (BC) gains estimated by three satellite products in relation to forest gains for each subregion defined by the IPCC Sixth Assessment Report (Fig. S4) during 2010-2019, for (a) IB L-VOD, (b) CCI and (c) Xu. Note that forest gains are derived from the GFW dataset and processed following the approach described in Yang *et al.*⁹.</figcaption></figure>

products, except for CCI, both L-VOD and Xu have relatively coarse spatial resolutions exceeding 10 km, making them susceptible to the issue of spatial heterogeneity.

We then collected annual forest cover data from the Brazilian Amazon to assess, in a side-by-side manner, the capability of the IB L-VOD to capture interannual variations in forest dynamics during 2010–2019 (Fig. 9). This comparison was motivated by the finding of Qin *et al.*²⁵, who reported a strong correlation between interannual forest area change and AGC change in the region. Importantly, the two datasets are completely independent. Furthermore, no one-to-one reference dataset currently exists to directly evaluate the interannual variability of satellite-based biomass estimates⁵⁶. It was found that the annual anomalies, represented by z-score (value – mean)/standard deviation), in both IB L-VOD and Xu were highly consistent with the interannual dynamics of forest area anomalies in regions where the forest area fraction (FAF) is lower than 50% (Fig. 9a-c). In these cases, the satellite-estimated biomass changes and forest area generally showed an increase before 2014, followed by a decrease during the well-known extreme drought period of 2015–2016²⁴, and subsequent recovery. However, in regions with high FAF levels, this pattern was generally only replicated before 2016. In the period following the
<figure><img src="image_18.png" /><figcaption>**Fig. 9** Relationships between forest area changes and biomass carbon changes from IB L-VOD (blue) and Xu (yellow) across different initial forest area fraction (FAF) intervals in 2010. r denotes the Spearman correlation between the respective biomass change product and the forest area change.</figcaption></figure>

2015-2016 drought, both IB L-VOD and forest area showed a continuous decline, whereas Xu exhibited a recovery rate similar to that of regions with lower FAF (Fig. 9d–f). Overall, IB L-VOD responded well to temporal fluctuations in forest area, exhibiting higher Spearman correlation values across different FAF levels compared to Xu, except at FAF levels of 90–100%, where relatively flat curves can be seen.

## Usage Notes

Continuous annual aboveground biomass carbon (AGC) data is essential for understanding the global carbon cycle and assessing the impact of climate change on forest, as they are closely linked to atmospheric CO₂ emissions from land use changes and fires, and CO₂ absorption by vegetation growth³. While combining forest inventory plots with optical satellite datasets can produce spatial AGC maps, this approach typically provides data for only a single year or a few discrete time points⁶⁵,⁶⁶. In contrast, L-VOD, which relies only on passive microwave satellite observations, enables the generation of continuous and consistent time series of annual AGC estimates. Moreover, the high penetration capabilities of low-frequency radiation through the canopy makes L-VOD less susceptible to saturation, which has motivated previous studies to use it for tracking AGC changes in tropical and global dense forests⁹,²³. We here developed a global annual AGC dataset at 25 km spatial resolution for 2010–2020 based on IB SMOS-IC L-VOD by implementing improved calibration methods and correcting for vegetation water effects. The accuracy assessments revealed that the IB L-VOD-derived AGC shows a very good spatial and temporal consistency with national forest inventory data and forest disturbance events, when compared to other mainstream satellite products. The annual AGC maps based on L-VOD are intended to support applications such as national and regional estimates of carbon stocks and their changes, integration into Earth System Models, and assessments of the global carbon budget. However, due to the lack of consensus on reference values for large-scale biomass change, we could not directly assess aboveground biomass carbon gains estimated from L-VOD. Instead, we examined the relationship between total biomass carbon gains and total area of forest gain, thereby indirectly assessing the responsiveness of IB AGC. Future efforts will still need to focus on finding more effective validation methods to comply with the guidelines for validation of aboveground woody biomass products⁶⁷.

Moreover, the coarse resolution of IB AGC (25 km) may limit its ability to capture small-scale biomass dynamics and introduce uncertainty during the calibration process due to pixel heterogeneity. Future studies may benefit from improving the spatial resolution of L-VOD to mitigate these limitations. Given that SMOS has already exceeded its expected mission lifetime by nearly ten years, in the case of a failure of the SMOS instrument, continuity could be ensured by merging SMOS and SMAP (Soil Moisture Active Passive) data⁵⁹. In addition, machine learning approaches could be used to integrate other satellite observations¹³, enabling both retrospective reconstruction and forward extension of the time series if the mission stops. Although total biomass carbon (BC) was not our primary focus, we provided a supplementary product estimated from the ratio between aboveground and belowground biomass based on Spawn *et al.*³⁰. Users could also apply other methods, such as the root-to-shoot ratio, to convert L-VOD derived AGC to BGC⁶⁸. Due to the impact of spatio-temporal
dynamic RFI, the coverage of IB AGC product is incomplete in some regions for some years, such as in China in 2010. To address these gaps, users could apply spatial gap-filling using neighboring pixels or use the method proposed by Yang *et al.*⁹, which approximates the average rate of change between the first valid observation and the target year for each pixel. It should be noted that the VOD-RWC correction and VOD-AGC calibration methods proposed in this study can also be applied to other long-term VOD datasets for estimating AGC⁶⁹,⁷⁰. It is expected that the IB AGC product developed in this study will offer an observational complement to existing biomass datasets and contribute to a better understanding of global vegetation carbon stocks and how they respond to climate change.

## Code availability

The analysis scripts used in this study are publicly available at https://github.com/pioaneerli123/IB-AGC-V1.

Received: 26 November 2024; Accepted: 26 June 2025;

Published online: 07 July 2025

## References

1. Houghton, R. Aboveground forest biomass and the global carbon balance. *Global Change Biology* **11**, 945–958 (2005).

2. Requena Suarez, D. *et al.* Estimating aboveground net biomass change for tropical and subtropical forests: Refinement of IPCC default rates using forest plot data. *Global Change Biology* **25**, 3609–3624 (2019).

3. GCOS. The Global Observing System for Climate: Implementation Needs gcos-244. (2022).

4. Carvalhais, N. *et al.* Global covariation of carbon turnover times with climate in terrestrial ecosystems. *Nature* **514**, 213–217 (2014).

5. Ciais, P. *et al.* Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system. *Biogeosciences* **11**, 3547–3602 (2014).

6. IPCC. Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (Intergovernmental Panel on Climate Change, 2019).

7. Santoro, M. *et al.* The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. *Earth System Science Data* **13**, 3927–3950 (2021).

8. Herold, M. *et al.* The role and need for space-based forest biomass-related measurements in environmental management and policy. *Surveys in Geophysics* **40**, 757–778 (2019).

9. Yang, H. *et al.* Global increase in biomass carbon stock dominated by growth of northern young forests over past decade. *Nature Geoscience* **16**, 886–892 (2023).

10. Pan, Y. *et al.* A large and persistent carbon sink in the world's forests. *Science* **333**, 988–993 (2011).

11. Liu, Y. Y. *et al.* Recent reversal in loss of global terrestrial biomass. *Nature Climate Change* **5**, 470–474 (2015).

12. Harris, N. L. *et al.* Global maps of twenty-first century forest carbon fluxes. *Nature Climate Change* **11**, 234–240 (2021).

13. Xu, L. *et al.* Changes in global terrestrial live biomass over the 21st century. *Science Advances* **7**, eabe9829 (2021).

14. Wang, J. A., Baccini, A., Farina, M., Randerson, J. T. & Friedl, M. A. Disturbance suppresses the aboveground carbon sink in North American boreal forests. *Nature Climate Change* **11**, 435–441 (2021).

15. Williams, C. A., Gu, H., MacLean, R., Masek, J. G. & Collatz, G. J. Disturbance and the carbon balance of US forests: A quantitative review of impacts from harvests, fires, insects, and droughts. *Global Planetary Change* **143**, 66–80 (2016).

16. Perugini, L. *et al.* Emerging reporting and verification needs under the Paris Agreement: How can the research community effectively contribute? *Environmental Science Policy* **122**, 116–126 (2021).

17. Grassi, G. *et al.* Harmonising the land-use flux estimates of global models and national inventories for 2000–2020. *Earth System Science Data* **15**, 1093–1114 (2023).

18. Santoro, M. & Cartus O. Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5. NERC EDS Centre for Environmental Data Analysis (2024).

19. Wigneron, J.-P. *et al.* Global carbon balance of the forest: satellite-based L-VOD results over the last decade. *Frontiers in Remote Sensing* **5**, 1338618 (2024).

20. Wigneron, J.-P. *et al.* SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives. *Remote Sensing of Environment* **254**, 112238 (2021).

21. Li, X. *et al.* Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products. *Remote Sensing of Environment* **253**, 112208 (2021).

22. Frappart, F. *et al.* Global monitoring of the vegetation dynamics from the vegetation optical depth (VOD): A review. *Remote Sensing* **12**, 2915 (2020).

23. Fan, L. *et al.* Satellite-observed pantropical carbon dynamics. *Nature Plants* **5**, 944–951 (2019).

24. Wigneron, J.-P. *et al.* Tropical forests did not recover from the strong 2015–2016 El Niño event. *Science Advances* **6**, eaay4603 (2020).

25. Qin, Y. *et al.* Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. *Nature Climate Change* **11**, 442–448 (2021).

26. Zhao, Z. *et al.* Central African biomass carbon losses and gains during 2010–2019. *One Earth* **7**, 506–519 (2024).

27. Feng, Y. *et al.* Global patterns and drivers of tropical aboveground carbon changes. *Nature Climate Change* **14**, 1064–1070 (2024).

28. Fan, L. *et al.* Siberian carbon sink reduced by forest disturbances. *Nature Geoscience* **16**, 56–62 (2023).

29. Yu, L. *et al.* Forest degradation contributes more to carbon loss than forest cover loss in North American boreal forests. *International Journal of Applied Earth Observation Geoinformation* **128**, 103729 (2024).

30. Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. *Scientific Data* **7**, 112 (2020).

31. Tian, F. *et al.* Coupling of ecosystem-scale plant water storage and leaf phenology observed by satellite. *Nature Ecology and Evolution* **2**, 1428–1435 (2018).

32. Konings, A. G., Rao, K. & Steele-Dunne, S. C. Macro to micro: microwave remote sensing of plant water content for physiology and ecology. *New Phytologist* **223**, 1166–1172 (2019).

33. Li, X. *et al.* Compared performances of SMOS-IC soil moisture and vegetation optical depth retrievals based on Tau-Omega and Two-Stream microwave emission models. *Remote Sensing of Environment* **236**, 111502 (2020).

34. Wigneron, J.-P. *et al.* Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. *Remote Sensing of Environment* **192**, 238–262 (2017).

35. Rodríguez-Fernández, N. J. *et al.* An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa. *Biogeosciences* **15**, 4627–4645 (2018).

36. Cui, T. *et al.* First assessment of optical and microwave remotely sensed vegetation proxies in monitoring aboveground carbon in tropical Asia. *Remote Sensing of Environment* **293**, 113619 (2023).
37. Ma, H. et al. Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations. *Remote Sensing of Environment* **231**, 111215 (2019).

38. Colliander, A. et al. Performance of SMOS soil moisture products over core validation sites. *IEEE Geoscience Remote Sensing Letters* **20**, 1–5 (2023).

39. Li, X. et al. Large live biomass carbon losses from droughts in the northern temperate ecosystems during 2016-2022. *Nature Communications* **16**, 1–13 (2025).

40. Al-Yaari, A. et al. Asymmetric responses of ecosystem productivity to rainfall anomalies vary inversely with mean annual rainfall over the conterminous United States. *Global Change Biology* **26**, 6959–6973 (2020).

41. Wang, H. et al. Seasonal variations in vegetation water content retrieved from microwave remote sensing over Amazon intact forests. *Remote Sensing of Environment* **285**, 113409 (2023).

42. Momen, M. et al. Interacting effects of leaf water potential and biomass on vegetation optical depth. *Journal of Geophysical Research: Biogeosciences* **122**, 3031–3046 (2017).

43. Zhang, Y., Zhou, S., Gentine, P. & Xiao, X. Can vegetation optical depth reflect changes in leaf water potential during soil moisture dry-down events? *Remote Sensing of Environment* **234**, 111451 (2019).

44. Yang, H. et al. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño. *Proceedings of the National Academy of Sciences* **119**, e2101388119 (2022).

45. Wigneron, J.-P. et al. Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band. *IEEE Transactions on Geoscience Remote Sensing* **42**, 416–425 (2004).

46. Dong, J. et al. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. *Remote Sensing of Environment* **84**, 393–410 (2003).

47. Fan, L.-y, Gao, Y.-z, Brück, H. & Bernhofer, C. Investigating the relationship between NDVI and LAI in semi-arid grassland in Inner Mongolia using *in-situ* measurements. *Theoretical Applied Climatology* **95**, 151–156 (2009).

48. Potithep S., Nasahara N., Muraoka H., Nagai S., Suzuki R. What is the actual relationship between LAI and VI in a deciduous broadleaf forest. *International Archives of the Photogrammetry, Remote Sensing Spatial Information Science* **38**, (2010).

49. Zhu, X. & Liu, D. Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series. *ISPRS Journal of Photogrammetry Remote Sensing* **102**, 222–231 (2015).

50. Le Maire, G. et al. MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass. *Remote Sensing of Environment* **115**, 2613–2625 (2011).

51. Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. *Remote Sensing of Environment* **58**, 257–266 (1996).

52. Bucci, S. J. et al. Processes preventing nocturnal equilibration between leaf and soil water potential in tropical savanna woody species. *Tree Physiology* **24**, 1119–1127 (2004).

53. Quan, X. et al. Global fuel moisture content mapping from MODIS. *International Journal of Applied Earth Observation Geoinformation* **101**, 102354 (2021).

54. Stoica, P. & Selen, Y. Model-order selection: a review of information criterion rules. *IEEE Signal Processing Magazine* **21**, 36–47 (2004).

55. Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. **2**, 827–835 (2018).

56. Araza, A. et al. Past decade above-ground biomass change comparisons from four multi-temporal global maps. *International Journal of Applied Earth Observation Geoinformation* **118**, 103274 (2023).

57. Rodda, S. R. et al. LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa. *Scientific Data* **11**, 334 (2024).

58. Potapov, P. et al. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. *Science Advances* **3**, e1600821 (2017).

59. Li, X. et al. The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations. *Remote Sensing of Environment* **282**, 113272 (2022).

60. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. *Science* **342**, 850–853 (2013).

61. Li, X. & Wigneron, J.-P. IB-AGC: Annual 25 km global live biomass carbon product from SMOS L-band passive microwave vegetation optical depth. Zenodo https://zenodo.org/records/15676177 (2025).

62. Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. *Science* **361**, 1108–1111 (2018).

63. Kim, J. E., Wang, J. A., Li, Y., Czimczik, C. I. & Randerson, J. T. Wildfire-induced increases in photosynthesis in boreal forest ecosystems of North America. *Global Change Biology* **30**, e17151 (2024).

64. Venter, Z., Cramer, M. & Hawkins, H.-J. Drivers of woody plant encroachment over Africa. *Nature Communications* **9**, 2272 (2018).

65. Saatchi, S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. *Proceedings of the National Academy of Sciences* **108**, 9899–9904 (2011).

66. Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. *Global Change Biology* **22**, 1406–1420 (2016).

67. Duncanson, L. et al. Aboveground woody biomass product validation good practices protocol. (2021).

68. Huang, Y. et al. A global map of root biomass across the world's forests. *Earth System Science Data* **13**, 4263–4274 (2021).

69. Skulovich, O., Li, X., Wigneron, J.-P. & Gentine, P. Global L-band equivalent AI-based vegetation optical depth dataset. *Scientific Data* **11**, 936 (2024).

70. Zotta, R.-M. et al. VODCA v2: multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring. *Earth System Science Data* **16**, 4573–4617 (2024).

71. Didan K. MODIS/Aqua Vegetation Indices 16-Day L3 Global 250m SIN Grid V061. (ed set NELPDAACd) (2021).

72. Hersbach, H. et al. The ERA5 global reanalysis. *Quarterly Journal of the Royal Meteorological Society* **146**, 1999–2049 (2020).

73. Dubayah, R. et al. GEDI L4B gridded aboveground biomass density, version 2. ORNL DAAC, (2022).

74. Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. *Science* **358**, 230–234 (2017).

75. FAO. Global Forest Resources Assessment 2020: Main report. (2020).

76. Grassi, G. et al. Carbon fluxes from land 2000–2020: bringing clarity on countries’ reporting. *Earth System Science Data* **2022**, 1–49 (2022).

77. Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. *Remote Sensing of Environment* **178**, 31–41 (2016).

## Acknowledgements

This study was supported by the RECCAP2 project, which is part of the ESA Climate Change Initiative (contract no. 4000123002/18/I-NB), and by the Centre National d'Études Spatiales (CNES, France) through its TOSCA program, which supports research activities in the context of the SMOS mission.
**Author contributions**

X.J.L., J.-P.W. and P.C. conceived and designed the overall study. X.J.L. performed analysis, and drafted the paper. All authors contributed to the interpretation of the results and the writing of the paper.

**Competing interests**

The authors declare no competing interests.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41597-025-05470-2.

**Correspondence** and requests for materials should be addressed to J.-P.W.

**Reprints and permissions information** is available at www.nature.com/reprints.

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

**Open Access** This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

© The Author(s) 2025