the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 May 2026
A Moderate-Resolution, Long-Term Global Radar-Based Forest Above-Ground Biomass Dataset from 1993 to 2020
Abstract. Understanding global carbon dynamics and budgets under climate change, land-use shifts, and increasing disturbances remains challenging due to the limitations of existing coarse spatial resolution and short-term or discontinuous biomass datasets. In this study, we generated a new global annual forest above-ground biomass dataset at 8.9 km spatial resolution from 1993 to 2020. This dataset is derived from satellite radar backscatter data and integrates background climate constraints to accounts for regional differences and improve the accuracy of global above-ground biomass mapping. Our dataset estimates an average global forest above-ground biomass carbon stock of 191 ± 2.5 PgC, aligning with other global estimates. We observed an increase in global forest above-ground biomass carbon stocks from 1993 to 2020 at a rate of 0.29 PgC yr−1. Tropical Africa, temperate and boreal forests are the primary contributors to global forest above-ground biomass carbon stock gains from 1993 to 2020. In contrast, gross above-ground biomass carbon losses are predominantly observed in tropical America and Asia forests, particularly since 2000. This long-term, temporally continuous, and moderate-resolution dataset provides a new benchmark for quantifying biomass carbon dynamics and integrating these processes into Earth System Models. The above-ground forest biomass dataset is openly accessible, alongside this manuscript.
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Status: open (until 01 Jul 2026)
- RC1: 'Comment on essd-2026-246', Anonymous Referee #1, 09 Jun 2026 reply
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A Moderate-Resolution, Long-Term Global Radar-Based Forest Above-Ground Biomass Dataset from 1993 to 2020 Liu Guohua, Ciais Philippe, Tao Shengli, Yang Hui, Xiao Chenwei, and Bastos Ana https://doi.org/10.5281/zenodo.19259660
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- 1
The authors present a new global forest above-ground biomass dataset spanning 1993–2020 at 8.9 km resolution, produced by training a random forest model on ESA-CCI biomass maps using radar backscatter and climate covariates as predictors. Compared to existing long-term products, which are VOD-based at ~25 km, the dataset offers a finer spatial resolution while retaining a near-30-year continuous annual record. This is achieved by exploiting the recently published radar backscatter record of Tao et al. (2023). The product is potentially a useful community resource, and the manuscript includes comparisons against other biomass products and field inventories. However, two important issues should be addressed before publication, which I outline below.
1. The product's central contribution is temporal — interannual to decadal biomass dynamics — but the model structure raises a concern about where that temporal signal originates. Two of the three predictors (MAT_bg, MAP_bg) are static background climate and cannot, by construction, generate any interannual variation; the authors further show that adding dynamic climate does not improve performance (Table 1). The temporal dynamics in the product therefore rest entirely on the radar backscatter input. Radar backscatter is, by its nature, sensitive to soil moisture and vegetation water content as well as to biomass; the authors' wet-season index reduces but does not remove this, and even the selected index retains a non-trivial correlation with soil moisture (Figure S3). Because the product's interannual signal comes only from radar, any residual moisture variability will project directly onto the reported biomass trends. Combined with the low standalone radar skill (test R² = 0.136), this raises the concern that part of the temporal signal may reflect moisture dynamics rather than biomass change. To be clear, I am not claiming radar is uninformative — with correlated predictors the variance explained is shared between radar and climate, and the large jump to R² = 0.833 when static climate is added may reflect climate carrying much of the spatial structure. The concern is specific to the temporal signal that distinguishes this dataset. The authors should (i) report variable importance for the selected model, and (ii) demonstrate that the interannual and decadal trends are driven by genuine radar-sensed biomass change rather than residual moisture variability — for example by relating the temporal anomalies back to the radar signal and to soil moisture directly.
2. The reported net sink of 0.44 PgC yr⁻¹ is a small residual between much larger, oppositely-signed gross gain (~0.84 PgC yr⁻¹) and gross loss (~−0.39 to −0.74 PgC yr⁻¹) terms, which makes it sensitive to systematic biases in the product. The quoted uncertainty of ±2.5 PgC (Eq. 1) captures only the spread across cross-validation iterations and does not propagate other potentially important sources — calibration bias (the slope of 0.36 against inventories), offsets at the ERS/ASCAT/QSCAT sensor transitions, and the fixed conversion factors (0.49 carbon fraction; root-to-shoot map) applied globally. It would strengthen the paper if the authors acknowledged more explicitly that the reported uncertainty is a lower bound reflecting model variance only, discussed these additional sources qualitatively even if they cannot be fully quantified, and tempered the net-flux claims accordingly. The 1997 artefact (Figure 3B) is a useful illustration here: it shows that a sensor transition can produce a spurious global biomass swing, so a brief comment on whether the other transitions were checked for similar (smaller) offsets would help reassure readers about the homogeneity of the trend record.
Minors comments
C-band saturation in dense forest acknowledged (l. 338–341) but underplayed given tropics = ~66% of stock.
Test R² inconsistent: 0.833 (Table 1) vs 0.883 (l. 213).