iSDAsoil Clay Content

ISDASOIL/Africa/v1/clay_content
Dataset-Verfügbarkeit
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Dataset-Anbieter
Earth Engine-Snippet
ee.Image("ISDASOIL/Africa/v1/clay_content")
Tags
Afrika Ton ISDA Boden
>

Beschreibung

Tongehalt in Bodentiefen von 0–20 cm und 20–50 cm, vorhergesagter Mittelwert und Standardabweichung. In Gebieten mit dichtem Dschungel (in der Regel in Zentralafrika) ist die Modellgenauigkeit gering. Daher können Artefakte wie Streifenbildung auftreten.

Die Vorhersagen der Bodeneigenschaften wurden von Innovative Solutions for Decision Agriculture Ltd. (iSDA) mit einer Pixelgröße von 30 m mithilfe von maschinellem Lernen in Kombination mit Fernerkundungsdaten und einem Trainingssatz von über 100.000 analysierten Bodenproben erstellt.

Weitere Informationen finden Sie in den FAQs und in der Dokumentation mit technischen Informationen. Wenn Sie ein Problem melden oder Support anfordern möchten, rufen Sie die iSDAsoil-Website auf.

Bänder

Pixelgröße
30 Meter

Bänder

Name Einheiten Min. Max. Pixelgröße Beschreibung
mean_0_20 % 0 84 Meter

Tonanteil, prognostizierter Mittelwert in 0–20 cm Tiefe

mean_20_50 % 0 78 Meter

Tonanteil, vorhergesagter Mittelwert in einer Tiefe von 20–50 cm

stdev_0_20 % 0 90 Meter

Tonanteil, Standardabweichung in 0–20 cm Tiefe

stdev_20_50 % 0 90 Meter

Tonanteil, Standardabweichung in 20–50 cm Tiefe

Nutzungsbedingungen

Nutzungsbedingungen

CC-BY-4.0

Zitate

Quellenangaben:
  • 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

Earth Engine nutzen

Code-Editor (JavaScript)

var mean_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#00204D" label="0-8" opacity="1" quantity="8"/>' +
  '<ColorMapEntry color="#002D6C" label="8-14" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#16396D" label="14-17" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#36476B" label="17-19" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#4B546C" label="19-21" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#5C616E" label="21-22" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#6C6E72" label="22-24" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#7C7B78" label="24-25" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#8E8A79" label="25-26" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#A09877" label="26-28" opacity="1" quantity="28"/>' +
  '<ColorMapEntry color="#B3A772" label="28-30" opacity="1" quantity="30"/>' +
  '<ColorMapEntry color="#C6B66B" label="30-31" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#DBC761" label="31-33" opacity="1" quantity="33"/>' +
  '<ColorMapEntry color="#F0D852" label="33-36" opacity="1" quantity="36"/>' +
  '<ColorMapEntry color="#FFEA46" label="36-70" opacity="1" quantity="40"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#00204D" label="0-8" opacity="1" quantity="8"/>' +
  '<ColorMapEntry color="#002D6C" label="8-14" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#16396D" label="14-17" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#36476B" label="17-19" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#4B546C" label="19-21" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#5C616E" label="21-22" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#6C6E72" label="22-24" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#7C7B78" label="24-25" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#8E8A79" label="25-26" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#A09877" label="26-28" opacity="1" quantity="28"/>' +
  '<ColorMapEntry color="#B3A772" label="28-30" opacity="1" quantity="30"/>' +
  '<ColorMapEntry color="#C6B66B" label="30-31" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#DBC761" label="31-33" opacity="1" quantity="33"/>' +
  '<ColorMapEntry color="#F0D852" label="33-36" opacity="1" quantity="36"/>' +
  '<ColorMapEntry color="#FFEA46" label="36-70" opacity="1" quantity="40"/>' +
 '</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="6"/>' +
 '</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="6"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

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

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

var visualization = {min: 0, max: 50};

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

Map.addLayer(converted.select(0), visualization, "Clay content, mean, 0-20 cm");
Im Code-Editor öffnen