iSDAsoil Sand Content

AI-generated Key Takeaways

• This dataset provides predicted mean and standard deviation of sand content at soil depths of 0-20 cm and 20-50 cm across Africa from 2001 to 2017.

• Soil property predictions were generated by iSDA using machine learning with remote sensing data and a large dataset of soil samples, resulting in a 30m pixel size.

• Users should be aware that model accuracy is lower in dense jungle areas, potentially showing artifacts like banding.

• Further technical details, FAQs, and support can be found on the iSDAsoil website.

• The dataset includes four bands: mean and standard deviation for both 0-20 cm and 20-50 cm depths, with values in percentage.

Dataset Availability

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

Dataset Provider

iSDA

Earth Engine Snippet

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

Tags

africa isda sand soil

Description

Sand content at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation. 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	%	2	94	meters	Sand content, predicted mean at 0-20 cm depth
mean_20_50	%	2	95	meters	Sand content, predicted mean at 20-50 cm depth
stdev_0_20	%	0	144	meters	Sand content, standard deviation at 0-20 cm depth
stdev_20_50	%	0	143	meters	Sand content, 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

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Code Editor (JavaScript)

var mean_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#00204D" label="0-31" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#002D6C" label="31-39" opacity="1" quantity="39"/>' +
  '<ColorMapEntry color="#16396D" label="39-43" opacity="1" quantity="43"/>' +
  '<ColorMapEntry color="#36476B" label="43-46" opacity="1" quantity="46"/>' +
  '<ColorMapEntry color="#4B546C" label="46-49" opacity="1" quantity="49"/>' +
  '<ColorMapEntry color="#5C616E" label="49-52" opacity="1" quantity="52"/>' +
  '<ColorMapEntry color="#6C6E72" label="52-54" opacity="1" quantity="54"/>' +
  '<ColorMapEntry color="#7C7B78" label="54-56" opacity="1" quantity="56"/>' +
  '<ColorMapEntry color="#8E8A79" label="56-58" opacity="1" quantity="58"/>' +
  '<ColorMapEntry color="#A09877" label="58-60" opacity="1" quantity="60"/>' +
  '<ColorMapEntry color="#B3A772" label="60-63" opacity="1" quantity="63"/>' +
  '<ColorMapEntry color="#C6B66B" label="63-65" opacity="1" quantity="65"/>' +
  '<ColorMapEntry color="#DBC761" label="65-68" opacity="1" quantity="68"/>' +
  '<ColorMapEntry color="#F0D852" label="68-71" opacity="1" quantity="71"/>' +
  '<ColorMapEntry color="#FFEA46" label="71-100" opacity="1" quantity="75"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#00204D" label="0-31" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#002D6C" label="31-39" opacity="1" quantity="39"/>' +
  '<ColorMapEntry color="#16396D" label="39-43" opacity="1" quantity="43"/>' +
  '<ColorMapEntry color="#36476B" label="43-46" opacity="1" quantity="46"/>' +
  '<ColorMapEntry color="#4B546C" label="46-49" opacity="1" quantity="49"/>' +
  '<ColorMapEntry color="#5C616E" label="49-52" opacity="1" quantity="52"/>' +
  '<ColorMapEntry color="#6C6E72" label="52-54" opacity="1" quantity="54"/>' +
  '<ColorMapEntry color="#7C7B78" label="54-56" opacity="1" quantity="56"/>' +
  '<ColorMapEntry color="#8E8A79" label="56-58" opacity="1" quantity="58"/>' +
  '<ColorMapEntry color="#A09877" label="58-60" opacity="1" quantity="60"/>' +
  '<ColorMapEntry color="#B3A772" label="60-63" opacity="1" quantity="63"/>' +
  '<ColorMapEntry color="#C6B66B" label="63-65" opacity="1" quantity="65"/>' +
  '<ColorMapEntry color="#DBC761" label="65-68" opacity="1" quantity="68"/>' +
  '<ColorMapEntry color="#F0D852" label="68-71" opacity="1" quantity="71"/>' +
  '<ColorMapEntry color="#FFEA46" label="71-100" opacity="1" quantity="75"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var stdev_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' +
  '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>' +
  '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
  '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>' +
  '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="7"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var stdev_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' +
  '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>' +
  '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
  '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>' +
  '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="7"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';
var raw = ee.Image("ISDASOIL/Africa/v1/sand_content");
Map.addLayer(
    raw.select(0).sldStyle(mean_0_20), {},
    "Sand content, mean visualization, 0-20 cm");
Map.addLayer(
    raw.select(1).sldStyle(mean_20_50), {},
    "Sand content, mean visualization, 20-50 cm");
Map.addLayer(
    raw.select(2).sldStyle(stdev_0_20), {},
    "Sand content, stdev visualization, 0-20 cm");
Map.addLayer(
    raw.select(3).sldStyle(stdev_20_50), {},
    "Sand content, stdev visualization, 20-50 cm");

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

var visualization = {min: 0, max: 3000};

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

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

Open in Code Editor

iSDAsoil Sand Content

Sand content at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation. 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. …

ISDASOIL/Africa/v1/sand_content, africa,isda,sand,soil

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

-35.22 -31.46 37.98 57.08

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