iSDAsoil Extractable Potassium

AI-generated Key Takeaways

• This dataset provides the predicted mean and standard deviation of extractable potassium in African soil at two depths (0-20 cm and 20-50 cm).

• The data covers the period from 2001 to 2017 and was produced by Innovative Solutions for Decision Agriculture Ltd.(iSDA).

• Pixel values require back-transformation using the formula exp(x/10)-1 to obtain the actual potassium values in parts per million (ppm).

• The dataset has a 30-meter resolution and may have lower accuracy with potential artifacts in dense jungle regions of central Africa.

• It is licensed under CC-BY-4.0 and available for exploration and analysis within Google Earth Engine.

Dataset Availability

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

Dataset Provider

iSDA

Earth Engine Snippet

ee.Image("ISDASOIL/Africa/v1/potassium_extractable")

Tags

africa isda soil

potassium

Description

Extractable potassium 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	80	meters	Potassium, extractable, predicted mean at 0-20 cm depth
mean_20_50	ppm	0	79	meters	Potassium, extractable, predicted mean at 20-50 cm depth
stdev_0_20	ppm	0	92	meters	Potassium, extractable, standard deviation at 0-20 cm depth
stdev_20_50	ppm	0	92	meters	Potassium, 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

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Code Editor (JavaScript)

var mean_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#0D0887" label="0-32.1" opacity="1" quantity="35"/>' +
  '<ColorMapEntry color="#350498" label="32.1-43.7" opacity="1" quantity="38"/>' +
  '<ColorMapEntry color="#5402A3" label="43.7-48.4" opacity="1" quantity="39"/>' +
  '<ColorMapEntry color="#7000A8" label="48.4-53.6" opacity="1" quantity="40"/>' +
  '<ColorMapEntry color="#8B0AA5" label="53.6-59.3" opacity="1" quantity="41"/>' +
  '<ColorMapEntry color="#A31E9A" label="59.3-65.7" opacity="1" quantity="42"/>' +
  '<ColorMapEntry color="#B93289" label="65.7-72.7" opacity="1" quantity="43"/>' +
  '<ColorMapEntry color="#CC4678" label="72.7-89" opacity="1" quantity="45"/>' +
  '<ColorMapEntry color="#DB5C68" label="89-98.5" opacity="1" quantity="46"/>' +
  '<ColorMapEntry color="#E97158" label="98.5-108.9" opacity="1" quantity="47"/>' +
  '<ColorMapEntry color="#F48849" label="108.9-120.5" opacity="1" quantity="48"/>' +
  '<ColorMapEntry color="#FBA139" label="120.5-133.3" opacity="1" quantity="49"/>' +
  '<ColorMapEntry color="#FEBC2A" label="133.3-163" opacity="1" quantity="51"/>' +
  '<ColorMapEntry color="#FADA24" label="163-199.3" opacity="1" quantity="53"/>' +
  '<ColorMapEntry color="#F0F921" label="163-1200" opacity="1" quantity="55"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#0D0887" label="0-32.1" opacity="1" quantity="35"/>' +
  '<ColorMapEntry color="#350498" label="32.1-43.7" opacity="1" quantity="38"/>' +
  '<ColorMapEntry color="#5402A3" label="43.7-48.4" opacity="1" quantity="39"/>' +
  '<ColorMapEntry color="#7000A8" label="48.4-53.6" opacity="1" quantity="40"/>' +
  '<ColorMapEntry color="#8B0AA5" label="53.6-59.3" opacity="1" quantity="41"/>' +
  '<ColorMapEntry color="#A31E9A" label="59.3-65.7" opacity="1" quantity="42"/>' +
  '<ColorMapEntry color="#B93289" label="65.7-72.7" opacity="1" quantity="43"/>' +
  '<ColorMapEntry color="#CC4678" label="72.7-89" opacity="1" quantity="45"/>' +
  '<ColorMapEntry color="#DB5C68" label="89-98.5" opacity="1" quantity="46"/>' +
  '<ColorMapEntry color="#E97158" label="98.5-108.9" opacity="1" quantity="47"/>' +
  '<ColorMapEntry color="#F48849" label="108.9-120.5" opacity="1" quantity="48"/>' +
  '<ColorMapEntry color="#FBA139" label="120.5-133.3" opacity="1" quantity="49"/>' +
  '<ColorMapEntry color="#FEBC2A" label="133.3-163" opacity="1" quantity="51"/>' +
  '<ColorMapEntry color="#FADA24" label="163-199.3" opacity="1" quantity="53"/>' +
  '<ColorMapEntry color="#F0F921" label="163-1200" opacity="1" quantity="55"/>' +
 '</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/potassium_extractable");
Map.addLayer(
    raw.select(0).sldStyle(mean_0_20), {},
    "Potassium extractable, mean visualization, 0-20 cm");
Map.addLayer(
    raw.select(1).sldStyle(mean_20_50), {},
    "Potassium extractable, mean visualization, 20-50 cm");
Map.addLayer(
    raw.select(2).sldStyle(stdev_0_20), {},
    "Potassium extractable, stdev visualization, 0-20 cm");
Map.addLayer(
    raw.select(3).sldStyle(stdev_20_50), {},
    "Potassium extractable, stdev visualization, 20-50 cm");

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

var visualization = {min: 0, max: 250};

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

Map.addLayer(converted.select(0), visualization, "Potassium extractable, mean, 0-20 cm");

Open in Code Editor

iSDAsoil Extractable Potassium

Extractable potassium 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/potassium_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

https://developers.google.com/earth-engine/datasets