Soil organic carbon (SOC) is very important in the vulnerable ecological environment of the Third Pole; however, data regarding the spatial distribution of SOC are still scarce and uncertain. Based on multiple environmental variables and soil profile data from 458 pits (depth of 0–1 m) and 114 cores (depth of 0–3 m), this study uses a machine-learning approach to evaluate the SOC storage and spatial distribution at different soil depths (0–30 cm, 0–50 cm, 0–100 cm, 0–200 cm, and 0–300 cm) in the frozen ground area of the Third Pole region. Our results provide information on the storage, patterns, and environmental controls of SOCSs at a 1 km2 scale for areas of frozen ground in the Third Pole region, thus providing a scientific basis for future studies pertaining to Earth system models.
Soil organic carbon data is stored in grids format, and the file name is "TP-SOC-d.tif", where d represents soil depth, for example, "TP-SOC-30.tif" represents the spatial distribution of soil organic carbon stocks in the Third Pole regions of the upper 30 cm depth interval.
Obtained through computer simulation.
Combined with high-resolution remote sensing data and interpolated meteorological data, three machine learning methods were used to evaluate SOC reserves and spatial distribution at different soil depths (0-30 cm, 0-50 cm, 0-100 cm, 0-200 cm and 0-300 cm) in the third polar permafrost region.
The data quality is good.
This work is licensed under a
Creative
Commons Attribution 4.0 International License.
CSTR:11738.11.NCDC.ZENODO.DB3964.2023
10.5281/zenodo.4293454