iSDAsoil Organic Carbon

  • This dataset provides soil organic carbon predictions for Africa at two depths (0-20 cm and 20-50 cm), including mean and standard deviation values.

  • The data covers the period from 2001 to 2017 and was produced by iSDA using machine learning and remote sensing data.

  • Predictions are provided at a 30-meter resolution and need back-transformation using a provided formula for analysis.

  • Model accuracy is reduced in dense jungle areas, potentially leading to visual artifacts.

  • The dataset is licensed under CC-BY-4.0 and users can find more information on the iSDAsoil website.
ISDASOIL/Africa/v1/carbon_organic
Dataset Availability
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Dataset Provider
Earth Engine Snippet
ee.Image("ISDASOIL/Africa/v1/carbon_organic")
Tags
africa carbon isda soil
carbon-organic

Description

Organic 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
30 meters

Bands

Name Units Min Max Pixel Size Description
mean_0_20 g/kg 1 46 meters

Carbon, organic, predicted mean at 0-20 cm depth
mean_20_50 g/kg 0 46 meters

Carbon, organic, predicted mean at 20-50 cm depth
stdev_0_20 g/kg 0 12 meters

Carbon, organic, standard deviation at 0-20 cm depth
stdev_20_50 g/kg 0 13 meters

Carbon, organic, 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

Explore with Earth Engine

Code Editor (JavaScript)

var mean_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#000004" label="0-2.3" opacity="1" quantity="12"/>' +
  '<ColorMapEntry color="#0C0927" label="2.3-3.5" opacity="1" quantity="15"/>' +
  '<ColorMapEntry color="#231151" label="3.5-4" opacity="1" quantity="16"/>' +
  '<ColorMapEntry color="#410F75" label="4-4.5" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#5F187F" label="4.5-5" opacity="1" quantity="18"/>' +
  '<ColorMapEntry color="#7B2382" label="5-5.7" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#982D80" label="5.7-6.4" opacity="1" quantity="20"/>' +
  '<ColorMapEntry color="#B63679" label="6.4-7.2" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#D3436E" label="7.2-8" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#EB5760" label="8-9" opacity="1" quantity="23"/>' +
  '<ColorMapEntry color="#F8765C" label="9-10" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#FD9969" label="10-11.2" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#FEBA80" label="11.2-12.5" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#FDDC9E" label="12.5-13.9" opacity="1" quantity="27"/>' +
  '<ColorMapEntry color="#FCFDBF" label="13.9-40" opacity="1" quantity="28"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#000004" label="0-2.3" opacity="1" quantity="12"/>' +
  '<ColorMapEntry color="#0C0927" label="2.3-3.5" opacity="1" quantity="15"/>' +
  '<ColorMapEntry color="#231151" label="3.5-4" opacity="1" quantity="16"/>' +
  '<ColorMapEntry color="#410F75" label="4-4.5" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#5F187F" label="4.5-5" opacity="1" quantity="18"/>' +
  '<ColorMapEntry color="#7B2382" label="5-5.7" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#982D80" label="5.7-6.4" opacity="1" quantity="20"/>' +
  '<ColorMapEntry color="#B63679" label="6.4-7.2" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#D3436E" label="7.2-8" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#EB5760" label="8-9" opacity="1" quantity="23"/>' +
  '<ColorMapEntry color="#F8765C" label="9-10" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#FD9969" label="10-11.2" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#FEBA80" label="11.2-12.5" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#FDDC9E" label="12.5-13.9" opacity="1" quantity="27"/>' +
  '<ColorMapEntry color="#FCFDBF" label="13.9-40" opacity="1" quantity="28"/>' +
 '</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="2"/>' +
  '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>' +
  '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>' +
  '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>' +
 '</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="2"/>' +
  '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>' +
  '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>' +
  '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var raw = ee.Image("ISDASOIL/Africa/v1/carbon_organic");
Map.addLayer(
    raw.select(0).sldStyle(mean_0_20), {},
    "Carbon, organic, mean visualization, 0-20 cm");
Map.addLayer(
    raw.select(1).sldStyle(mean_20_50), {},
    "Carbon, organic, mean visualization, 20-50 cm");
Map.addLayer(
    raw.select(2).sldStyle(stdev_0_20), {},
    "Carbon, organic, stdev visualization, 0-20 cm");
Map.addLayer(
    raw.select(3).sldStyle(stdev_20_50), {},
    "Carbon, organic, stdev visualization, 20-50 cm");

var converted = raw.divide(10).exp().subtract(1);

var visualization = {min: 0, max: 20};

Map.setCenter(25, -3, 2);

Map.addLayer(converted.select(0), visualization, "Carbon, organic, mean, 0-20 cm");
Open in Code Editor
Organic 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_organic, africa,carbon,isda,soil
2001-01-01T00:00:00Z/2017-01-01T00:00:00Z
-35.22 -31.46 37.98 57.08
Google Earth Engine