- Dataset Availability
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- Dataset Provider
- iSDA
- Earth Engine Snippet
-
ee.Image("ISDASOIL/Africa/v1/ph") - Tags
Description
pH at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation.
Pixel values must be back-transformed with x/10.
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 | Min | Max | Description |
|---|---|---|---|
mean_0_20 |
35 | 103 | pH, predicted mean at 0-20 cm depth |
mean_20_50 |
35 | 102 | pH, predicted mean at 20-50 cm depth |
stdev_0_20 |
0 | 18 | pH, standard deviation at 0-20 cm depth |
stdev_20_50 |
0 | 18 | pH, standard deviation at 20-50 cm depth |
Terms of Use
Terms of Use
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="#CC0000" label="3.5-4.6" opacity="1" quantity="46"/>' +
'<ColorMapEntry color="#FF0000" label="4.6-4.9" opacity="1" quantity="49"/>' +
'<ColorMapEntry color="#FF5500" label="4.9-5.2" opacity="1" quantity="52"/>' +
'<ColorMapEntry color="#FFAA00" label="5.2-5.4" opacity="1" quantity="54"/>' +
'<ColorMapEntry color="#FFFF00" label="5.4-5.5" opacity="1" quantity="55"/>' +
'<ColorMapEntry color="#D4FF2B" label="5.5-5.6" opacity="1" quantity="56"/>' +
'<ColorMapEntry color="#AAFF55" label="5.6-5.7" opacity="1" quantity="57"/>' +
'<ColorMapEntry color="#80FF80" label="5.7-5.9" opacity="1" quantity="59"/>' +
'<ColorMapEntry color="#55FFAA" label="5.9-6" opacity="1" quantity="60"/>' +
'<ColorMapEntry color="#2BFFD5" label="6-6.2" opacity="1" quantity="62"/>' +
'<ColorMapEntry color="#00FFFF" label="6.2-6.3" opacity="1" quantity="63"/>' +
'<ColorMapEntry color="#00AAFF" label="6.3-6.6" opacity="1" quantity="66"/>' +
'<ColorMapEntry color="#0055FF" label="6.6-6.8" opacity="1" quantity="68"/>' +
'<ColorMapEntry color="#0000FF" label="6.8-7.1" opacity="1" quantity="71"/>' +
'<ColorMapEntry color="#0000CC" label="7.1-10.5" opacity="1" quantity="76"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#CC0000" label="3.5-4.6" opacity="1" quantity="46"/>' +
'<ColorMapEntry color="#FF0000" label="4.6-4.9" opacity="1" quantity="49"/>' +
'<ColorMapEntry color="#FF5500" label="4.9-5.2" opacity="1" quantity="52"/>' +
'<ColorMapEntry color="#FFAA00" label="5.2-5.4" opacity="1" quantity="54"/>' +
'<ColorMapEntry color="#FFFF00" label="5.4-5.5" opacity="1" quantity="55"/>' +
'<ColorMapEntry color="#D4FF2B" label="5.5-5.6" opacity="1" quantity="56"/>' +
'<ColorMapEntry color="#AAFF55" label="5.6-5.7" opacity="1" quantity="57"/>' +
'<ColorMapEntry color="#80FF80" label="5.7-5.9" opacity="1" quantity="59"/>' +
'<ColorMapEntry color="#55FFAA" label="5.9-6" opacity="1" quantity="60"/>' +
'<ColorMapEntry color="#2BFFD5" label="6-6.2" opacity="1" quantity="62"/>' +
'<ColorMapEntry color="#00FFFF" label="6.2-6.3" opacity="1" quantity="63"/>' +
'<ColorMapEntry color="#00AAFF" label="6.3-6.6" opacity="1" quantity="66"/>' +
'<ColorMapEntry color="#0055FF" label="6.6-6.8" opacity="1" quantity="68"/>' +
'<ColorMapEntry color="#0000FF" label="6.8-7.1" opacity="1" quantity="71"/>' +
'<ColorMapEntry color="#0000CC" label="7.1-10.5" opacity="1" quantity="76"/>' +
'</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/ph");
Map.addLayer(
raw.select(0).sldStyle(mean_0_20), {},
"ph, mean visualization, 0-20 cm");
Map.addLayer(
raw.select(1).sldStyle(mean_20_50), {},
"ph, mean visualization, 20-50 cm");
Map.addLayer(
raw.select(2).sldStyle(stdev_0_20), {},
"ph, stdev visualization, 0-20 cm");
Map.addLayer(
raw.select(3).sldStyle(stdev_20_50), {},
"ph, stdev visualization, 20-50 cm");
var converted = raw.divide(10);
var visualization = {min: 4, max: 8};
Map.setCenter(25, -3, 2);
Map.addLayer(converted.select(0), visualization, "ph, mean, 0-20 cm");