iSDAsoil Extractable Phosphorus

  • This dataset provides predictions of extractable phosphorus at soil depths of 0-20 cm and 20-50 cm across Africa from 2001 to 2017.

  • The data, provided by iSDA, includes both the predicted mean and standard deviation of extractable phosphorus at a 30-meter pixel size.

  • Pixel values require a specific back-transformation using the formula exp(x/10)-1 for accurate interpretation.

  • The accuracy of the model is lower in areas with dense jungle, potentially leading to visual artifacts like banding.
ISDASOIL/Africa/v1/phosphorus_extractable
Dataset Availability
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Dataset Provider
Earth Engine Snippet
ee.Image("ISDASOIL/Africa/v1/phosphorus_extractable")
Tags
africa isda soil
phosphorus

Description

Extractable phosphorus 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 ppm 1 55 meters

Phosphorus, extractable, predicted mean at 0-20 cm depth
mean_20_50 ppm 0 52 meters

Phosphorus, extractable, predicted mean at 20-50 cm depth
stdev_0_20 ppm 0 19 meters

Phosphorus, extractable, standard deviation at 0-20 cm depth
stdev_20_50 ppm 0 20 meters

Phosphorus, 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

Explore with Earth Engine

Code Editor (JavaScript)

var mean_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#0D0887" label="0-2.7" opacity="1" quantity="13"/>' +
  '<ColorMapEntry color="#350498" label="2.7-3" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#5402A3" label="3-3.5" opacity="1" quantity="15"/>' +
  '<ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>' +
  '<ColorMapEntry color="#8B0AA5" label="4-4.5" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#A31E9A" label="4.5-5" opacity="1" quantity="18"/>' +
  '<ColorMapEntry color="#B93289" label="5-5.7" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#CC4678" label="5.7-6.4" opacity="1" quantity="20"/>' +
  '<ColorMapEntry color="#DB5C68" label="6.4-7.2" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#E97158" label="7.2-8" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#F48849" label="8-9" opacity="1" quantity="23"/>' +
  '<ColorMapEntry color="#FBA139" label="9-10" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#FEBC2A" label="10-11.2" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#FADA24" label="11.2-12.5" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#F0F921" label="12.5-125" opacity="1" quantity="27"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#0D0887" label="0-2.7" opacity="1" quantity="13"/>' +
  '<ColorMapEntry color="#350498" label="2.7-3" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#5402A3" label="3-3.5" opacity="1" quantity="15"/>' +
  '<ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>' +
  '<ColorMapEntry color="#8B0AA5" label="4-4.5" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#A31E9A" label="4.5-5" opacity="1" quantity="18"/>' +
  '<ColorMapEntry color="#B93289" label="5-5.7" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#CC4678" label="5.7-6.4" opacity="1" quantity="20"/>' +
  '<ColorMapEntry color="#DB5C68" label="6.4-7.2" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#E97158" label="7.2-8" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#F48849" label="8-9" opacity="1" quantity="23"/>' +
  '<ColorMapEntry color="#FBA139" label="9-10" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#FEBC2A" label="10-11.2" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#FADA24" label="11.2-12.5" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#F0F921" label="12.5-125" opacity="1" quantity="27"/>' +
 '</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/phosphorus_extractable");
Map.addLayer(
    raw.select(0).sldStyle(mean_0_20), {},
    "Phosphorus extractable, mean visualization, 0-20 cm");
Map.addLayer(
    raw.select(1).sldStyle(mean_20_50), {},
    "Phosphorus extractable, mean visualization, 20-50 cm");
Map.addLayer(
    raw.select(2).sldStyle(stdev_0_20), {},
    "Phosphorus extractable, stdev visualization, 0-20 cm");
Map.addLayer(
    raw.select(3).sldStyle(stdev_20_50), {},
    "Phosphorus extractable, stdev visualization, 20-50 cm");

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

var visualization = {min: 0, max: 15};

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

Map.addLayer(converted.select(0), visualization, "Phosphorus extractable, mean, 0-20 cm");
Open in Code Editor
Extractable phosphorus 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/phosphorus_extractable, africa,isda,soil
2001-01-01T00:00:00Z/2017-01-01T00:00:00Z
-35.22 -31.46 37.98 57.08
Google Earth Engine