iSDAsoil Total Carbon

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")

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

Resolution
30 meters

Bands

Name	Units	Min	Max	Description
mean_0_20	g/kg	0	58	Carbon, total, predicted mean at 0-20 cm depth
mean_20_50	g/kg	0	55	Carbon, total, predicted mean at 20-50 cm depth
stdev_0_20	g/kg	0	151	Carbon, total, standard deviation at 0-20 cm depth
stdev_20_50	g/kg	0	150	Carbon, total, standard deviation at 20-50 cm depth

Terms of Use

Terms of Use

CC-BY-4.0

Citations

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

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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

iSDAsoil Total Carbon

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 …

ISDASOIL/Africa/v1/carbon_total, africa,aluminium,isda,soil

2001-01-01T00:00:00Z/2017-01-01T00:00:00Z

-35.22 -31.46 37.98 57.08

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