There is a newer version of the record available.

Published October 31, 2018 | Version v0.1
Dataset Open

Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

  • 1. Envirometrix Ltd

Description

Soil pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. 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,
  • ph.h2o = variable: soil pH in H2O,
  • usda.4c1a2a = determination method: laboratory method code,
  • m = mean value,
  • 250m = spatial resolution / block support: 250 m,
  • b10..10cm = vertical reference: 10 cm depth below surface,
  • 1950..2017 = time reference: period 1950-2017,
  • v0.1 = version number: 0.1,

Files

sol_ph.h2o_usda.4c1a2a_m.png

Files (20.9 GB)

Name Size Download all
md5:0cb2a79a8d4d8154d7ff97f17730190a
943.1 kB Preview Download
md5:dbd92eb1baf2f9437bfea52cf12d662b
4.6 kB Download
md5:c5e16904d24032948a3914e332e4851a
1.8 GB Preview Download
md5:12ee6f5b8189734c8ad40afd04346d76
1.8 GB Preview Download
md5:82cad89d37a1d06640a2122ae4436d2a
1.8 GB Preview Download
md5:784e69558b0ed313f0ae8f9e1bfb4c1b
1.8 GB Preview Download
md5:3aaf3721a13f79cd60c4527a48545684
1.8 GB Preview Download
md5:72aeb0d42069568d6b38e69242dc5611
1.8 GB Preview Download
md5:b446a1204f2894db01b752af8eef5d37
1.7 GB Preview Download
md5:d8f48f973e6d6c80e83af908b730e77e
1.7 GB Preview Download
md5:e2be8b8c69b05e4225e9bb19d192faea
1.7 GB Preview Download
md5:0ce9f1f7cb2c63787e298ca2d72874ff
1.7 GB Preview Download
md5:84819a6318b124402ee475d307ebb86f
1.7 GB Preview Download
md5:fa26cff19e691047d7254de579e2d63a
1.7 GB Preview Download

Additional details

References

  • USDA-NRCS, (2014) Laboratory Methods Manual (SSIR 42). U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center.
  • Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748.