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Determining the dynamics of organic carbon in subsoil (SOC, depth of 20–100 cm) is important with respect to the global C cycle and warming mitigation. However, there is still a huge knowledge gap in the dynamics of spatiotemporal changes in SOC in this layer. Combining traditional depth functions and machine-learning methods, we achieved soil β values and SOC dynamics at high resolution for global ecosystems (cropland, grassland, and forestland). First, quantified the spatial variability characteristics of soil β values and driving factors by analyzing 1221 soil profiles (0–100 cm) of globally distributed field observations. Then, based on multiple environmental variables and soil profile data, we mapped the grid-level soil β values with machine-learning approaches. Lastly, we evaluated the SOC density spatial distribution in different soil layers to determine the subsoil SOC stocks of various ecosystems. The subsoil SOC density values of cropland, grassland, and forestland were 63.8, 83.3, and 100.4 Mg ha–1, respectively. SOC density decreased with increasing depth, ranging from 5.6 to 30.8 Mg ha–1 for cropland, 7.5 to 40.0 Mg ha–1 for grassland, and 9.6 to 47.0 Mg ha–1 for forestland. The global subsoil SOC stock was 912 Pg C (cropland, grassland, and forestland were 67, 200, and 644 Pg C), in which an average of 54 % resided in the top 0–100 cm of the soil profile. Our results provide information on the vertical distribution and spatial patterns of SOC density at a 10 km resolution for areas of Global ecosystems, which providing a scientific basis for future studies pertaining to Earth system models. The dataset is open-access and available at https://doi.org/10.5281/zenodo.10846543 (Wang et al., 2024). [ABSTRACT FROM AUTHOR]