| 24 Jul 2025
A High-Resolution, Long-Term Global Radar-Based Above-Ground Biomass Dataset from 1993 to 2020
Abstract. Understanding global carbon dynamics and budgets under climate change, land-use shifts, and increasing disturbances re- mains 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 above-ground biomass carbon (AGC) dataset at 8 km spatial resolution from 1993 to 2020. This dataset is derived from satellite radar backscatter data and integrates vegetation and climate information, such as tree cover, tree density, and background climate data, to enhance the accuracy of global AGC mapping. Our dataset estimates an average global above-ground carbon stock of 378 PgC, aligning with other global estimates. We observe a slight gross increase of 1.18 PgC in global vegetation above-ground biomass carbon stocks from 1993 to 2020, with relatively stable variation. This reflects a balance between above-ground biomass carbon gains and losses across different biomes. Temperate and boreal forests are the primary contributors to global vegetation above-ground biomass carbon gains from 1993 to 2020, with increases of 0.4 and 0.5 PgC, respectively. In contrast, gross above-ground biomass carbon losses are predominantly observed in global tropical forests (-10.7 PgC) and global shrublands (-1.0 PgC). This suggests non-forest vegetation may offset the large above-ground biomass losses in tropical forests. Notably, El Niño events in 2015/16 triggered significant pantropical AGC losses of approximately -2.86 PgC, and regions with reported tree mortality events (Hammond et al., 2022) exhibited local AGC density declines of -0.34 MgC/ha. This long-term, temporally continuous, and moderate-resolution dataset provides a valuable resource for understanding biomass carbon dynamics and integrating these processes into Earth System Models. The AGC dataset is openly accessible, alongside with this manuscript.
The authors present a dataset of aboveground carbon density (AGC) derived from a long record of satellite scatterometers and auxiliary datasets. The data is provided at a pixel size of approximately 8 km. Thereof several trends are discussed with fluctuations due to major El Niño events occurred within the time interval of the acquisitions. The investigation tries to provide new evidences on the carbon cycle since 1992. After reading this manuscript, I have severe doubts concerning the validity of the dataset and find the interpretation of the results very qualitative. Some of the interpretations are furthermore either misleading or ambiguous. In the current form, the manuscript is not suitable for publication. It is suggested that the study is revised first. Personally, I encourage the authors to resubmit after having addresses the issues pointed to in this review. Below the authors can find a list of general comments followed by more line-to-line specific comments.
Specific comments.
L80. What does “covering most global land areas” mean?
L81. The data were regridded to a pixel size of 8 km (not resolution). Make sure that throughout the paper you do not refer to resolution but grid cell size or pixel size. What was the purpose of regridding?
L83. Please revise references as they mostly looked at passive microwave data.
L86. Please be more specific about the detection of outliers.
L88. What is the pixel size of the map by Tootchi et al.?
L89. Please explain the rational according to which pixels with a given fraction of peatland had to be masked out? Does this include pixels with trees growing on peatland?
L90. What is your definition of “vegetation”?
L90. Is Harper the correct reference for the CCI land cover map?
L102. Strictly speaking the mean temperature cannot be derived from the min and max values. Please rephrase.
L113. Replace “temporate” with “temperate”.
L114. Before the pixel size was expressed in km, Here in degree. Please use one single unit throughout the paper.
L114. Please explain how you could average a map that consists of a classification.
L120. What is the pixel size of the tree mortality dataset (if applicable)? Could you also specify a range of extent of these events? Are they much larger than the pixel size of the radar data?
L129. Can you be more specific on selecting 3 years (which?) to train the model and 1 (which?) to test. If the training set includes data in the test set, the validation is not independent because the backscatter data was already touched during training.
L143. I suppose that you are predicting annual AGB rather than AGB changes.
L153. Why not calling C_sink Delta_C instead? I have difficulties to understand the meaning of a negative C_sink value.
L158-167. Please provide some illustrative results that show the agreement between predicted and reference AGB.
Figure 1. What is the reference for the biomes? Are the uncertainties negligible so that they are not shown in panel (c)?
Figure 2. Panel (B). I assume that the loss of stocks and the recovery in 2014-2018 is associated to the El Nino event. Do you have any means to confirm the magnitude? Could it be that the fluctuation was mostly driven by moisture and the overall magnitude of the loss/gain cycle is smaller?
L272, 278 and 279. Since no definition of the biomass variable mapped was provided, it is confusing to read that AGCD was first attributed to forests, then to global vegetation area and finally to woody vegetation.
L275-276. The explanation makes no sense. Radar/radiometers does not sense anything below a few centimeters of the ground surface (unless the surface is extremely dry). As such, none can sense “under-ground” biomass (whatever “under-ground” biomass means).
L278. What is with forests growing on peatlands? Are they included or excluded?
L281 and 285. References to supplementary figures were not updated upon submission.
Table 2. What kind of processing was applied to account for different definitions of biomass in each of the studies?
L310. I suppose that the author means an increase of the AGC stock.
L349, 356 and 406. What does “radar scatter data” mean?
L358-364. The authors provide three explanations for why the data product in 1997 different compared to adjacent years. I am not convinced that the explanations hold true. First, it is unclear why “sensor anomalies” and “anomalous radar signals” (what are these actually?) would lead to a severe drop of the estimates. They could well lead to the opposite. Second, I understand that reference data for 1997 probably do not exist and so the result for this particular year cannot be validated. However, is it plausible that the El Nino event in the same year has an immediate effect on the biomass estimates given that the biomass was predicted from an average value of all ERS observations taken in 1997?
L364. Why only in Africa? And why just 1997 if the explanation is the decay of quality of the radar signal?
L366. I would be careful with this interpretation. The dataset by Besnard et al is based on the same type of the data used to predict biomass in this study but the differences between the datasets are large. Besnard et al. and Santoro et al. were validated with reference data at multiple scales. The dataset proposed here has not been validated so that this interpretation needs thorough revision. An in-depth comparison of datasets is suggested.
L373. A single reference to a paper published in 1989 is insufficient to prove that traditional approaches have been widely used.
L375-377. This interpretation can be applied to any RS-based map. As such, it is not unique to this study.
L408. It is referred to non-forest ecosystems which have not been discussed throughout the paper.