Published November 2, 2018
| Version v0.2
Dataset
Open
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
Description
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail here. Antarctica is not included.
To access and visualize maps use: OpenLandMap.org
If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:
- Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/issues
- General questions and comments: https://disqus.com/home/forums/landgis/
All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:
- sol = theme: soil,
- clay.wfraction = variable: sand weight fraction,
- usda.3a1a1a = 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,
- v0.2 = version number: 0.2,
Files
landGIS_clay_content.jpg
Files
(26.3 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:f2cbcd470a84d3b8c8c90c081f9efe73
|
572.9 kB | Preview Download |
|
md5:e4849f67676e63cb2cafd22fa17f9c0c
|
3.8 kB | Download |
|
md5:8b19eead4ba17a8fed278eb4c0f3c8e5
|
4.6 kB | Download |
|
md5:5c6ab29f9068a9fae746b5aa02d2d535
|
2.0 GB | Preview Download |
|
md5:7e5aa510c652f5735109e41f03674ff7
|
2.0 GB | Preview Download |
|
md5:a8f0d36ced5f5a023f5a058feaa10291
|
2.0 GB | Preview Download |
|
md5:9b302e6412f6658a7ced6371d73d8744
|
2.0 GB | Preview Download |
|
md5:e0211bf7020ca7aa54916163416ea283
|
2.0 GB | Preview Download |
|
md5:5e6353b50b9770faa347f3e57c7462c9
|
2.0 GB | Preview Download |
|
md5:81d4f11752c4d1c03e992a546ebdc381
|
2.0 GB | Preview Download |
|
md5:f859dd0c2f646ef796b43bad76915961
|
2.0 GB | Preview Download |
|
md5:374375478fc163e0f2de6c80421b943c
|
2.0 GB | Preview Download |
|
md5:3fbb78a3995d072d4333372328157ef0
|
2.0 GB | Preview Download |
|
md5:ec8b8dc0f7e0c529e08a63f1d3ce36ac
|
2.0 GB | Preview Download |
|
md5:78bb65be4839573a793d0d369b52782f
|
2.0 GB | Preview Download |
|
md5:a8f358160b266c6b8e3c880c9e1ca8c5
|
2.0 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.
- Hengl, T., MacMillan, R.A., (2019). Predictive Soil Mapping with R. OpenGeoHub foundation, Wageningen, the Netherlands, 370 pages, www.soilmapper.org, ISBN: 978-0-359-30635-0.