
- Dataset-Verfügbarkeit
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- Dataset-Anbieter
- iSDA
- Tags
Beschreibung
Schluffgehalt in Bodentiefen von 0–20 cm und 20–50 cm, vorhergesagter Mittelwert und Standardabweichung.
Pixelwerte müssen mit exp(x/10)-1
rücktransformiert werden.
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 |
% | 1 | 61 | Meter | Schluffgehalt, vorhergesagter Mittelwert in 0–20 cm Tiefe |
mean_20_50 |
% | 0 | 62 | Meter | Schluffgehalt, vorhergesagter Mittelwert in 20–50 cm Tiefe |
stdev_0_20 |
% | 0 | 38 | Meter | Schluffgehalt, Standardabweichung in 0–20 cm Tiefe |
stdev_20_50 |
% | 0 | 38 | Meter | Schluffgehalt, Standardabweichung in 20–50 cm Tiefe |
Nutzungsbedingungen
Nutzungsbedingungen
Zitate
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="0-7" opacity="1" quantity="7"/>' + '<ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>' + '<ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>' + '<ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>' + '<ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>' + '<ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>' + '<ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>' + '<ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>' + '<ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>' + '<ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>' + '<ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>' + '<ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>' + '<ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>' + '<ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#00204D" label="0-7" opacity="1" quantity="7"/>' + '<ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>' + '<ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>' + '<ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>' + '<ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>' + '<ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>' + '<ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>' + '<ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>' + '<ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>' + '<ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>' + '<ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>' + '<ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>' + '<ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>' + '<ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>' + '</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="4.19000000000005"/>' + '</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="4.19000000000005"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/silt_content"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Silt content, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Silt content, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Silt content, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Silt content, 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, "Silt content, mean, 0-20 cm");