iSDAsoil extractable Aluminium

ISDASOIL/Africa/v1/aluminium_extractable
Dataset Availability
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Dataset Provider
Earth Engine Snippet
ee.Image("ISDASOIL/Africa/v1/aluminium_extractable")
Tags
africa aluminium isda soil

Description

Extractable aluminium 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.

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.

In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen.

Bands

Resolution
30 meters

Bands

Name Units Min Max Description
mean_0_20 ppm 3 80

Aluminium, extractable, predicted mean at 0-20 cm depth
mean_20_50 ppm 4 79

Aluminium, extractable, predicted mean at 20-50 cm depth
stdev_0_20 ppm 1 53

Aluminium, extractable, standard deviation at 0-20 cm depth
stdev_20_50 ppm 1 51

Aluminium, extractable, 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

  • 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

DOIs

Explore with Earth Engine

Code Editor (JavaScript)

var mean_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' +
  '<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' +
  '<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' +
  '<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' +
  '<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' +
  '<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' +
  '<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' +
  '<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' +
  '<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' +
  '<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' +
  '<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' +
  '<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' +
  '<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' +
  '<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' +
  '<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' +
  '<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' +
  '<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' +
  '<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' +
  '<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' +
  '<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' +
  '<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' +
  '<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' +
  '<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' +
  '<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' +
  '<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' +
  '<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' +
  '<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var stdev_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>' +
  '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' +
  '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' +
  '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' +
  '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var stdev_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>' +
  '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' +
  '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' +
  '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' +
  '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

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

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

var converted = raw.divide(10).exp().subtract(1);
Map.addLayer(
    converted.select(0), {min: 0, max: 100},
    "Aluminium, extractable, mean, 0-20 cm");
Open in Code Editor
Extractable aluminium 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. 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 …
ISDASOIL/Africa/v1/aluminium_extractable, africa,aluminium,isda,soil
2001-01-01T00:00:00Z/2017-01-01T00:00:00Z
-35.22 -31.46 37.98 57.08
Google Earth Engine