the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jun 2025
High-resolution global map (100 m) of soil organic carbon reveals critical ecosystems for carbon storage
Abstract. Uncertainty in soil organic carbon (SOC) stocks and fluxes resulting from land disturbance and recovery processes remains a significant challenge for closing the global carbon budget, accurately quantifying the land carbon sink, and assessing restoration carbon credits in nature-based climate solutions. To address this, we develop a spatially resolved SOC estimate at a 1-hectare resolution globally, aligning with the scale of land-use disturbances, to significantly improve carbon accounting accuracy and reduce uncertainty across multiple use cases. We compile and harmonize a global SOC inventory, incorporating 84,880 (30 cm depth) and 44,304 (100 cm depth) measurements. Additionally, we identify high-resolution remote sensing and in situ spatial covariates to map SOC using advanced, biome-specific machine learning algorithms.
We measure global SOC stocks of 1,049 Pg C at 30 cm and 2,822 Pg C at 100 cm. Our results reveal a 31 % increase in SOC at 30 cm and a 45 % increase at 100 cm compared to the average of prior estimates. Our model indicates that peatlands including peat-in-soil mosaics store 146 Pg C at 30 cm depth and 344 Pg C at 100 cm depth, accounting for 14 % and 12 % of global SOC stocks respectively. Mangrove ecosystems have some of the highest soil carbon densities among global biomes, and hold 1.3 Pg C at 30 cm depth and 4.4 Pg C at 100 cm depth, despite covering a relatively small global area. We find that biome-level SOC estimates strongly depend on biome area and its changes over time. Our analysis indicates that annual wildfire dynamics and shifts in agricultural land can influence SOC by 132 Pg C and 140 Pg C at 30 cm, and by 345 Pg C and 368 Pg C at 100 cm, representing approximately 13 % of the global stocks. The SOC maps from this study, including pixel-level 95 % confidence intervals to quantify model uncertainty, are hosted on a Zenodo repository. These data will be made publicly available upon publication to support large-scale carbon accounting and integration by the scientific and policy communities. The repository is accessible through the reviewer link (Creze et al., 2025): https://zenodo.org/records/15391412?preview=1&token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImNjZjk2YTM5LTQyM2ItNDZiMC1iY2RlLTg0ZTA1ZjU1MDZjNSIsImRhdGEiOnt9LCJyYW5kb20iOiIxMjc1YmI0OTZhOTNiMmQyNTIxYjYyNzRiM2ZlZjBmMyJ9.M5VUSwR4GkeoKV1Kno1v3b3qLUAzErns1Zh6u0om2HhVDrnxcjKJS3WCOVAoJlSyxt-5Kbc809apXwYmAnMqyQ.
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RC1: 'Comment on essd-2025-294', Anonymous Referee #1, 20 Jul 2025
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This study leveraged a rich collection of global soil organic carbon (SOC) data to create an updated, high-resolution map of the spatial distribution of SOC storage at the top 30 cm and 100 cm. The motivation for developing a spatially resolved, high-resolution soil C map for better land management, C accounting, and global C monitoring is strong. The resulting map can be valuable to the urgent need of accurate monitoring and estimation of soil carbon removal impacts through climate solutions around agricultural and natural ecosystems. Overall, this work is a good contribution to the soil community, but I have a few concerns about the delivery of results and the writing. Therefore, I recommend this manuscript to be published on ESSD if a major revision can fully address the main concerns.
First, the good things:
I appreciate that the authors harmonized and cleaned up a massive collection of layered soil data rigorously. For example, they used only samples with bulk density measurements, adjusted measurement values according to sampling protocols, and filtered out redundant points. The authors dealt with SOC stock calculations and layered data at different depths responsibly.
The authors also fitted separate models for peatland and mangroves, which seems reasonable. This approach implies an assumption that relationships between SOC storage and predictor layers are different for these two ecosystems compared to the rest of global ecosystems.
Major concern 1:My first major concern is that the entire article showed lots of summarized total SOC storage at biome, regional, and global level, but none of these estimates come with an uncertainty range or confidence interval in the main text or the supplemental material. Although pixel level uncertainties were shown as main figures, the authors should still report uncertainties on the estimated global and regional SOC stocks in the abstract and throughout the result sections. This should be easy given the authors already fitted 20 models from bootstrapped training data. The CIs (and bootstrap distributions) can be derived once the regional and global estimates were calculated from each of the 20 models. The lack of regional uncertainties makes some of the conclusions unconvincing. For example, in section “3.2.1 Global fires”, the authors concluded that adding fire as a predictor layer increased global top 30cm SOC but decreased top 1m SOC. However, the difference for SOC at 1m is only 1% of the total estimated SOC (28 out of 2822, line 552-553). I don’t know whether the CIs of the estimated 2850 PgC and 2822 PgC overlap with each other. If so, this evidence can’t convince me that “fire can be a significant source of carbon emission”. Another concern about this paragraph (line 551-555) is that, although adding fire as a predictor seems ecologically reasonable, I’d still expect to see the authors show that the model performance increased after adding fire as a predictor, in order to make that final conclusion. Without proving fire as a statistically justifiable predictor, this result might point to some artefacts rather than an improvement in the model due to fire data. Another way to look into fire’s effect is to predict the global SOC stock from the fitted model (with the fire layer), in a scenario where no fire happens on Earth, and then compare the no-fire prediction to global SOC stock estimate with real fire frequencies (Perhaps this is what you did. Am I misunderstanding?).
Major concern 2:
It seems like land cover is not an explicit predictor in the model. It gives me two questions.
(1) It is hard for me to understand that without this predictor, how does the model discern different SOC contents between a forest and a nearby cropland that shares essentially the same environmental condition? Perhaps you expect the Landsat bands to give such information implicitly?(2) The peatland and mangrove models were fitted by points within their remote sensing extents. Is it possible to find ground-truth information on whether a soil sampling belongs to peatland or mangroves? Because small-scale farming can make patchy drained lands within such systems (as you mentioned), it is likely that some points within the peatland/mangrove extent weren’t in fact peatland or mangrove, raising potential bias to the biome-specific models. It would be great if the authors could validate the peatland/mangrove extent with their ground truth land cover—if you have some site-level information from the data source, what proportion of peatland/mangrove sites were and weren’t included in the peatland and mangrove masks? And how many sites that weren’t peatland/mangrove end up within the masks?
Major concern 3:
The soil data were sampled at different times spanning 1950s to 2020s, but the goal of this work is to represent a static map of global SOC stock distribution. It is common and maybe justifiable to use all historical data for mapping a static global SOC stock distribution, but I wish the authors could extend more discussion on how they expect readers to use this data product. The authors motivated this work with the recent need for soil carbon removal and land management, but how will this map of static SOC stock estimation help with these motivations, building upon the previous global SOC maps? I was excited to read an insightful reasoning of how this work is needed in the context of climate solution, policy making, and land management (which was pitched as the motivation), but I find it lacking in both the introduction and discussion. I hope the authors can elaborate more explicitly and clearly on the real impact and value of this data product, acknowledging that land use management and carbon removal programs would deal with SOC change over a short period of time (5-10 years). Some relevant literature on the perspective of natural climate solutions and carbon removal include https://www.nature.com/articles/s41558-024-02182-0 and https://www.nature.com/articles/s41467-023-44425-2.
Major concern 4:
There are no plots showing the effect of each covariate on SOC storage in the fitted model. I understand if the work doesn’t want to emphasize the fitted relationships in the model (which can be messy to interpret), but I think it should be reported in the supplemental material for transparency. A group of partial dependence plots for each biome-specific model would suffice, so that the readers don’t wonder if model predictions are driven by one or two strong but non-physical effects.
Minor comments:
Line 22-24, Abstract. “Our analysis indicates that annual wildfire dynamics and shifts in agricultural land can influence SOC by 132 Pg C and 140 Pg C at 30 cm, and by 345 Pg C and 368 Pg C at 100 cm, representing approximately 13% of the global stocks.”
These numbers seem to be calculated from the total estimated SOC stock in global areas that fall within the agricultural and fire-prone extent (line 619). Indeed, the theoretical maximum potential of SOC loss driven by any disturbances is always bounded by the total SOC stored in a given area, so the sentence is technically correct. However, this sentence reads like “we formally analyzed the effect of recent/near-future fire and agricultural dynamics on SOC change, which show that these activity poses a readily threat to this much SOC stock in the next several decades”. This does not accurately summarize the relevant results (line 619). In the fire-prone pixels defined by “average annual number of burned days >1” (line 296-297), such fire dynamics very likely do not pose an immediate threat to the entire soil C pool in every fire-prone pixel (the author also pointed out multiple times that fire has mixed effects to SOC change yet to be understood). The same point applies to agricultural activities. To avoid misrepresenting the main result of this work, please adjust the phrasing of this sentence. Maybe something like “estimate shows that XXX PgC of soil carbon sits in fire-prone area and/or area with ongoing agricultural activities…”
Line 505. “Our study suggests considerably greater carbon storage in the Amazon Basin.”
Is it because your study better predicts the large carbon storage inthe Amazon peatland (your next paragraph)? If so, explain explicitly here.
Line 551-555.
See major concern 1.
Line 685 “Our data indicates that 35% of the Cerrado is used for agriculture (72/204 Mha)”
And Line 689 “Our map highlights high fire activity in the Matopiba region”
If I understand correctly, the agricultural land extent is cited from another product, and the fire activity extent is indicated by the MODIS fire frequency map. How is that “your map” and your data? (I would’ve assumed these terms to refer to your SOC data product in this manuscript.) It kind of confuses and distracts readers from focusing on what’s truly your valuable map and your data product —the SOC stock maps at two depths.
Line 699-703
The authors have already discussed the mixed and unknown impact of fire on soil in the global fire section and the grassland section (lines 556-565, lines 424-426). Coming after these sections, these lines read redundant. Perhaps all of these can be consolidated better into the global fire section (the paragraph of lines 556-565)
Citation: https://doi.org/10.5194/essd-2025-294-RC1
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