Soil is an important part of the global terrestrial ecosystem and represents the largest terrestrial organic carbon pool with the longest turnover time (Amundson, 2001). This is especially true in areas of frozen ground, including permafrost and seasonally frozen ground. In cold environments, soil accumulates substantial organic carbon due to slow decomposition rates and repeated freeze–thaw cycles (Fan et al., 2012; Li et al., 2020). It has been reported that more than half of the world's soil organic carbon (SOC) is stored in permafrost regions (Hugelius et al., 2014; Ping et al., 2015). Even slight changes in the decomposition of the SOC pool in permafrost regions might lead to significant changes in the atmospheric CO2 concentration, which plays an important role in regulating and stabilizing the carbon balance of global ecosystems (Schuur et al., 2015). Therefore, it is of great significance to accurately estimate the storage and spatial distribution of SOC in regions of frozen ground in order to study the carbon cycle of this ecosystem as well as global change.

As the “roof of the world”, the Third Pole is the area of frozen ground at the highest average altitude in the middle and low latitudes of the Northern Hemisphere. The Third Pole is also one of the most sensitive areas with respect to global climate change and has a warming rate that is approximately twice the global average (Stocker et al., 2013). In the past few decades, permafrost in the Third Pole region has experienced obvious degradation (Mu et al., 2020b; Ran et al., 2018; Turetsky et al., 2019; Wu et al., 2012). Permafrost degradation will not only cause serious geological disasters and affect engineering construction in cold areas, but it will also accelerate the decomposition of the huge SOC pool stored in permafrost (Cheng and Wu, 2007; Cheng et al., 2019; Ding et al., 2021). Moreover, it will emit a large amount of greenhouse gases into the atmosphere, thus increasing the rate of climate change in the future (Schuur et al., 2015). Therefore, accurate estimates of the SOC storage and spatial distribution in the areas of frozen ground in the Third Pole region have become important for Earth system modeling. Such estimates are widely used to study the carbon cycle of this ecosystem and global change (Koven et al., 2011; Lombardozzi et al., 2016; McGuire et al., 2018).

Early studies were mostly based on data from China's national soil survey and were combined with regional vegetation–soil maps to estimate the SOC pool for a certain vegetation type or relatively small area (Wang et al., 2002; Zeng et al., 2004). Up until 2008, the Chinese part of the Qinghai–Tibet Plateau (QTP) was taken as an independent geographical unit to estimate the SOC pool in the upper 100 cm of the soil profile (Tian et al., 2008; Wu et al., 2008). However, these studies did not distinguish between regions of permafrost and seasonally frozen ground. In recent years, based on soil profile data and vegetation–soil maps, some studies have estimated the SOC pool in the QTP permafrost region (Mu et al., 2015; Zhao et al., 2018; Jiang et al., 2019). The aforementioned studies improved our understanding of SOC storage in the Third Pole region, but estimation results of 0–3 m SOC pool have large uncertainties, ranging from 17.1 to 40.9 Pg. In addition, the large-scale maps of vegetation and soil types used in these studies were associated with large uncertainties because they were created years ago and have a low spatial resolution, thus leading to potentially large errors in the estimated total SOC pools (Mishra et al., 2013; Mu et al., 2020a). Recently, considerable progress has been made in digital soil mapping methods. Spatial interpolation, linear regression, and machine learning have been widely used to simulate the spatial distribution of SOC in the permafrost region of the QTP (Ding et al., 2016, 2019; Wang et al., 2020; Yang et al., 2008). These studies have provided new spatial data and improved the prediction accuracy of SOC compared with earlier studies. However, few studies to date have systematically assessed SOC pools across areas of seasonally frozen ground in the Third Pole region, which limits many investigations requiring SOC data for these areas.

To evaluate the size and high-resolution spatial patterns of SOC stocks in the Third Pole region, we carried out a large-scale field-sampling plan that covered representative permafrost zones over the region's bioclimatic gradient, including a large unpopulated area with harsh natural conditions. A total of 200 soil pits were excavated, most of which were deeper than 2 m. In addition, we collected field-measured SOCS data for the Third Pole region from relevant literature published between 2000 and 2016 (Ding et al., 2016; Song et al., 2016; Xu et al., 2019; Yang et al., 2008). By combining high-resolution remotely sensed data and interpolated meteorological datasets, we simulated the spatial distribution of SOCSs in the Third Pole region by three machine-learning methods and calculated the SOC storage of specific soil intervals (0–30, 0–50, 0–100, 0–200, and 0–300 cm). The results provide basic data for Earth system modeling and reference methods for studying the spatial distribution of soil elements under complex terrain.

In this study, we provided the new version of 1 km resolution maps of SOCS across the Third Pole at 0–300 cm depth intervals, which largely makes up for the deficiencies of previous studies (Ding et al., 2016, 2019; Wang et al., 2020). On the one hand, our predictions have higher resolution than those studies. Take an example and focus on a 4.5×104 km2 local area situated in the Budongquan area of Qinghai province, China (Fig. 8). It can be seen from the excerpts of the map that our prediction is much more detailed than previous studies. Thus, our predictions better represented spatial variation of the SOCS across the Third Pole region, especially for those regions with large heterogeneity. On the other hand, these reports focused mostly on the permafrost regions rather than the whole Third Pole (Ding et al., 2016; Wang et al., 2020). To date, few studies have investigated the SOC storage and spatial patterns in areas of seasonally frozen ground in the Third Pole region. In this study, we created high spatial resolution data of SOCS distribution in the whole Third Pole by compiling all the field data and using machine-learning methods, thus providing more accurate data than previous studies.

In addition, our predictions were much more accurate than the existing global SOC datasets. Figure 9 shows accuracy assessments of our predictions, the SoilGrids250m from Hengl et al. (2017), and the WISE30sec SOCS data from Batjes (2016) at 0–2 m depth intervals based on the 213 SOC stock data from Ding et al. (2016) and field investigations. We found that our prediction had a higher R2 value and lower RMSE value than SoilGrids250m and WISE30sec. The lowest accuracy was found for the WISE30sec maps, showing the advantage of digital soil mapping based on machine learning over conventional mapping method based on the vegetation–soil units (Liu et al., 2020). The lower accuracy of SoilGrids250m than our predictions is mainly because of serious overestimation of bulk density, as well as the neglected influence of coarse gravel content (Hengl et al., 2017). Soil profile data used in SoilGrids250m at the Third Pole region are mainly from China's second national soil survey, which lacked accurate information on coarse-gravel content and bulk density (Shi and Song, 2016). In addition, almost all of these soil profiles are within 1 m depth, which could be a great instability in calculating the deeper SOC by SoilGrids250m. Moreover, the global model building could be less accurate than the regional model building when focusing on a regional extent (Vitharana et al., 2019; Liu et al., 2020). Consequently, our predictions were much more accurate than the existing maps of SOCS.

Our study provides new and more accurate data on SOC storage and spatial patterns for a depth interval of 0–3 m at a 1 km resolution over the Third Pole region, thus providing basic data for future studies pertaining to Earth system modeling. We note that a lack of deep soil pits in forest, shrub, and cropland ecosystems (Fig. S2) means some uncertainties in the estimation of deep SOC pools remain; however, the collective area of these ecosystems accounts for < 6% of the total area of the Third Pole region and may have a relatively small influence on total SOC pools (Fig. S1). Regardless, there is a need for large-scale soil surveys that include these areas in order to obtain more accurate information on the SOC storage and distribution in the Third Pole region. Furthermore, regional SOC pools are affected by many other factors, such as soil moisture (Wu et al., 2016) and grazing activities (Zhou et al., 2017), which were not considered in our study due to lack of high-resolution data with a high accuracy. Future work should consider the influence of these factors on SOC at a regional scale to obtain more accurate datasets.

The datasets of SOC stocks distribution in GeoTiff format are available at 10.5281/zenodo.4293454 (Wang et al., 2020). The file name is “TP-SOC-d.tif”, where d represents soil depth; for example, “TP-SOC-30.tif” represents the spatial distribution of SOC stocks in the Third Pole regions of the upper 30 cm depth interval.

This study simulated the spatial pattern of the SOCS over the Third Pole region, and systematically estimated the SOC storage (46.18 Pg) at a depth interval of 0–3 m for the first time. Our results demonstrated that combining multi-environmental factors with machine-learning techniques (RF, SVM, and GBRT) can offer an effective and powerful modeling approach for mapping the spatial patterns of SOC. Furthermore, this study provided datasets of SOCS and SOC storage for permafrost and seasonally frozen ground at different soil depths (0–30, 0–50, 0–100, 0–200, and 0–300 cm) across the Third Pole region. These datasets can be used to modify existing Earth system models and improve prediction accuracy, as well as also serve as a reference for policymakers to formulate more effective carbon budget management strategies.