iSDAsoil Total Carbon
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Dataset Availability
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Dataset Provider
iSDA
Earth Engine Snippet
ee.Image("ISDASOIL/Africa/v1/carbon_total")
open_in_new
Tags
africa
aluminium
isda
soil
Description
Total carbon at soil depths of 0-20 cm and 20-50 cm,
predicted mean and standard deviation.
Pixel values must be back-transformed with exp(x/10)-1.
In areas of dense jungle (generally over central Africa), model accuracy is
low and therefore artifacts such as banding (striping) might be seen.
Soil property predictions were made by
Innovative Solutions for Decision Agriculture Ltd. (iSDA)
at 30 m pixel size using machine learning coupled with remote sensing data
and a training set of over 100,000 analyzed soil samples.
Further information can be found in the
FAQ and
technical information documentation . To submit an issue or request support, please visit
the iSDAsoil site .
Bands
Pixel Size
Bands
Name
Units
Min
Max
Pixel Size
Description
mean_0_20g/kg
0
58
meters
Carbon, total, predicted mean at 0-20 cm depth
mean_20_50g/kg
0
55
meters
Carbon, total, predicted mean at 20-50 cm depth
stdev_0_20g/kg
0
151
meters
Carbon, total, standard deviation at 0-20 cm depth
stdev_20_50g/kg
0
150
meters
Carbon, total, standard deviation at 20-50 cm depth
Citations
Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients
mapped at 30 m spatial resolution using two-scale ensemble machine learning.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
Explore with Earth Engine
Important:
Earth Engine is a platform for petabyte-scale scientific analysis and visualization of
geospatial datasets, both for public benefit and for business and government users.
Earth Engine is free to use for research, education, and nonprofit use. To get started, please
register for Earth Engine access.
Code Editor (JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' +
'<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' +
'<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' +
'<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' +
'<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' +
'<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' +
'<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' +
'<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' +
'<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' +
'<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' +
'<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/carbon_total" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Carbon, total, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Carbon, total, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Carbon, total, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Carbon, total, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 10 ). exp (). subtract ( 1 );
var visualization = { min : 0 , max : 60 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Carbon, total, mean, 0-20 cm" );
Open in Code Editor
[null,null,[],[[["\u003cp\u003eThe ISDASOIL/Africa/v1/carbon_total dataset provides predictions for total carbon content in African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eData includes predicted mean and standard deviation of total carbon, requiring back-transformation using \u003ccode\u003eexp(x/10)-1\u003c/code\u003e for actual values.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset covers the period from 2001 to 2017 and has a 30-meter resolution.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is potentially lower in dense jungle areas, leading to possible artifacts like banding.\u003c/p\u003e\n"],["\u003cp\u003eiSDA developed the dataset using machine learning and remote sensing data, coupled with extensive soil sample analysis.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Total Carbon\n\nDataset Availability\n: 2001-01-01T00:00:00Z--2017-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [iSDA](https://isda-africa.com/)\n\nTags\n:\n[africa](/earth-engine/datasets/tags/africa) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\nTotal carbon at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Pixel Size | Description |\n|---------------|-------|-----|-----|------------|-----------------------------------------------------|\n| `mean_0_20` | g/kg | 0 | 58 | meters | Carbon, total, predicted mean at 0-20 cm depth |\n| `mean_20_50` | g/kg | 0 | 55 | meters | Carbon, total, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | g/kg | 0 | 151 | meters | Carbon, total, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | g/kg | 0 | 150 | meters | Carbon, total, standard deviation at 20-50 cm depth |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\n### Citations\n\nCitations:\n\n- Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients\n mapped at 30 m spatial resolution using two-scale ensemble machine learning.\n Sci Rep 11, 6130 (2021).\n [doi:10.1038/s41598-021-85639-y](https://doi.org/10.1038/s41598-021-85639-y)\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar mean_0_20 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#000004\" label=\"0-2\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"5.7-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"10-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"19.1-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"21.2-23.5\" opacity=\"1\" quantity=\"32\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"23.5-26.1\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"26.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"29-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"32.1-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"35.6-40\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar mean_20_50 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#000004\" label=\"0-2\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"5.7-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"10-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"19.1-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"21.2-23.5\" opacity=\"1\" quantity=\"32\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"23.5-26.1\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"26.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"29-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"32.1-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"35.6-40\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar stdev_0_20 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#fde725\" label=\"low\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"6\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar stdev_20_50 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#fde725\" label=\"low\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"6\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/carbon_total\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Carbon, total, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Carbon, total, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Carbon, total, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Carbon, total, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 60};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Carbon, total, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_carbon_total) \n[iSDAsoil Total Carbon](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_total) \nTotal carbon at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were ... \nISDASOIL/Africa/v1/carbon_total, africa,aluminium,isda,soil \n2001-01-01T00:00:00Z/2017-01-01T00:00:00Z \n-35.22 -31.46 37.98 57.08 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://isda-africa.com/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_total)"]]
