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Dataset Open Access

Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution

Tomislav Hengl

Soil organic carbon stock in kg/m2 for 5 standard depth intervals (0–10, 10–30, 30–60, 60–100 and 100–200 cm) at 250 m resolution. Derived using soil organic carbon content, bulk density and coarse fragments, predicted from point data at 6 standard depths. Processing steps are described in detail here. Antartica is not included.

To access and visualize maps use: https://landgis.opengeohub.org

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:

  • sol = theme: soil,
  • organic.carbon.stock = variable: soil organic carbon stock in kg/m2,
  • msa.kgm2 = determination method: derived from organic carbon content, bulk density and coarse fragments,
  • m = mean value,
  • 250m = spatial resolution / block support: 250 m,
  • b0..10cm = vertical reference: 0-10 cm layer below surface,
  • 1950..2017 = time reference: period 1950-2017,
  • v0.2 = version number: 0.2,

Files (10.0 GB)
Name Size
sol_organic.carbon.stock_msa.kgm2_m.qml
md5:821d047df2fefef4c12964a8fe9ee0a8
2.6 kB Download
sol_organic.carbon.stock_msa.kgm2_m.sld
md5:ab09d1a160f14c732c23fe959a1b80be
2.2 kB Download
sol_organic.carbon.stock_msa.kgm2_m_250m_b0..10cm_1950..2017_v0.2.tif
md5:6197f477d8215655811f913f98495763
1.4 GB Download
sol_organic.carbon.stock_msa.kgm2_m_250m_b10..30cm_1950..2017_v0.2.tif
md5:7f5fbb339616e4bb57f4c66beba06a26
1.8 GB Download
sol_organic.carbon.stock_msa.kgm2_m_250m_b100..200cm_1950..2017_v0.2.tif
md5:44df098d70067db37c5ece3cc4022ec8
2.6 GB Download
sol_organic.carbon.stock_msa.kgm2_m_250m_b30..60cm_1950..2017_v0.2.tif
md5:a2a3927f27a46489b2b1f5fa40e3600d
2.0 GB Download
sol_organic.carbon.stock_msa.kgm2_m_250m_b60..100cm_1950..2017_v0.2.tif
md5:97720ee58f90c49445eb9f5839eac062
2.2 GB Download
  • Hengl, T., de Jesus, J.M., Heuvelink, G.B., Gonzalez, M.R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B. and Guevara, M.A., (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), p.e0169748. https://doi.org/10.1371/journal.pone.0169748

  • Sanderman, J., Hengl, T., Fiske, G., (2017). The soil carbon debt of 12,000 years of human land use. PNAS, https://dx.doi.org/10.1073/pnas.1706103114

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