iSDAsoil Organic Carbon
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Dataset-Verfügbarkeit
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Dataset-Anbieter
iSDA
Earth Engine-Snippet
ee.Image("ISDASOIL/Africa/v1/carbon_organic")
open_in_new
Tags
Afrika
Kohlenstoff
ISDA
Boden
CO₂-organisch
Beschreibung
Organischer Kohlenstoff 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
Bänder
Name
Einheiten
Min.
Max.
Pixelgröße
Beschreibung
mean_0_20g/kg
1
46
Meter
Kohlenstoff, organisch, vorhergesagter Mittelwert in 0–20 cm Tiefe
mean_20_50g/kg
0
46
Meter
Organischer Kohlenstoff, vorhergesagter Mittelwert in 20–50 cm Tiefe
stdev_0_20g/kg
0
12
Meter
Kohlenstoff, organisch, Standardabweichung bei 0–20 cm Tiefe
stdev_20_50g/kg
0
13
Meter
Organischer Kohlenstoff, Standardabweichung in 20–50 cm Tiefe
Nutzungsbedingungen
Nutzungsbedingungen
CC-BY-4.0
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
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Die Earth Engine kann kostenlos für Forschungs-, Bildungs- und gemeinnützige Zwecke verwendet werden. Registrieren Sie sich für den Zugriff auf die Earth Engine , um loszulegen.
Code-Editor (JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2.3" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#0C0927" label="2.3-3.5" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#231151" label="3.5-4" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#410F75" label="4-4.5" opacity="1" quantity="17"/>' +
'<ColorMapEntry color="#5F187F" label="4.5-5" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#7B2382" label="5-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#982D80" label="5.7-6.4" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#B63679" label="6.4-7.2" opacity="1" quantity="21"/>' +
'<ColorMapEntry color="#D3436E" label="7.2-8" opacity="1" quantity="22"/>' +
'<ColorMapEntry color="#EB5760" label="8-9" opacity="1" quantity="23"/>' +
'<ColorMapEntry color="#F8765C" label="9-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#FD9969" label="10-11.2" opacity="1" quantity="25"/>' +
'<ColorMapEntry color="#FEBA80" label="11.2-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#FDDC9E" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#FCFDBF" label="13.9-40" opacity="1" quantity="28"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2.3" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#0C0927" label="2.3-3.5" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#231151" label="3.5-4" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#410F75" label="4-4.5" opacity="1" quantity="17"/>' +
'<ColorMapEntry color="#5F187F" label="4.5-5" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#7B2382" label="5-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#982D80" label="5.7-6.4" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#B63679" label="6.4-7.2" opacity="1" quantity="21"/>' +
'<ColorMapEntry color="#D3436E" label="7.2-8" opacity="1" quantity="22"/>' +
'<ColorMapEntry color="#EB5760" label="8-9" opacity="1" quantity="23"/>' +
'<ColorMapEntry color="#F8765C" label="9-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#FD9969" label="10-11.2" opacity="1" quantity="25"/>' +
'<ColorMapEntry color="#FEBA80" label="11.2-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#FDDC9E" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#FCFDBF" label="13.9-40" opacity="1" quantity="28"/>' +
'</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/carbon_organic" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Carbon, organic, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Carbon, organic, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Carbon, organic, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Carbon, organic, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 10 ). exp (). subtract ( 1 );
var visualization = { min : 0 , max : 20 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Carbon, organic, mean, 0-20 cm" );
Im Code-Editor öffnen
[null,null,[],[[["\u003cp\u003eThis dataset provides soil organic carbon predictions for Africa at two depths (0-20 cm and 20-50 cm), including mean and standard deviation values.\u003c/p\u003e\n"],["\u003cp\u003eThe data covers the period from 2001 to 2017 and was produced by iSDA using machine learning and remote sensing data.\u003c/p\u003e\n"],["\u003cp\u003ePredictions are provided at a 30-meter resolution and need back-transformation using a provided formula for analysis.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is reduced in dense jungle areas, potentially leading to visual artifacts.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is licensed under CC-BY-4.0 and users can find more information on the iSDAsoil website.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Organic Carbon\n\nDataset Availability\n: 2001-01-01T00:00:00Z--2017-01-01T00:00:00Z\n\nDataset Provider\n:\n\n\n [iSDA](https://isda-africa.com/)\n\nTags\n:\n [africa](/earth-engine/datasets/tags/africa) [carbon](/earth-engine/datasets/tags/carbon) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \ncarbon-organic \n\n#### Description\n\nOrganic carbon at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `exp(x/10)-1`.\n\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\n\nSoil property predictions were made by\n[Innovative Solutions for Decision Agriculture Ltd. (iSDA)](https://isda-africa.com/)\nat 30 m pixel size using machine learning coupled with remote sensing data\nand a training set of over 100,000 analyzed soil samples.\n\nFurther information can be found in the\n[FAQ](https://www.isda-africa.com/isdasoil/faq/) and\n[technical information documentation](https://www.isda-africa.com/isdasoil/technical-information/). To submit an issue or request support, please visit\n[the iSDAsoil site](https://isda-africa.com/isdasoil).\n\n### Bands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Pixel Size | Description |\n|---------------|-------|-----|-----|------------|-------------------------------------------------------|\n| `mean_0_20` | g/kg | 1 | 46 | meters | Carbon, organic, predicted mean at 0-20 cm depth |\n| `mean_20_50` | g/kg | 0 | 46 | meters | Carbon, organic, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | g/kg | 0 | 12 | meters | Carbon, organic, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | g/kg | 0 | 13 | meters | Carbon, organic, standard deviation at 20-50 cm depth |\n\n### Terms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\n### Citations\n\nCitations:\n\n- Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients\n mapped at 30 m spatial resolution using two-scale ensemble machine learning.\n Sci Rep 11, 6130 (2021).\n [doi:10.1038/s41598-021-85639-y](https://doi.org/10.1038/s41598-021-85639-y)\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar mean_0_20 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#000004\" label=\"0-2.3\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2.3-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"3.5-4\" opacity=\"1\" quantity=\"16\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"4-4.5\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"4.5-5\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"5-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"5.7-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"6.4-7.2\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"7.2-8\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"8-9\" opacity=\"1\" quantity=\"23\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"9-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"10-11.2\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"11.2-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"13.9-40\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar mean_20_50 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#000004\" label=\"0-2.3\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2.3-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"3.5-4\" opacity=\"1\" quantity=\"16\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"4-4.5\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"4.5-5\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"5-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"5.7-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"6.4-7.2\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"7.2-8\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"8-9\" opacity=\"1\" quantity=\"23\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"9-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"10-11.2\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"11.2-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"13.9-40\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar stdev_0_20 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#fde725\" label=\"low\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar stdev_20_50 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#fde725\" label=\"low\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/carbon_organic\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Carbon, organic, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Carbon, organic, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Carbon, organic, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Carbon, organic, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 20};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Carbon, organic, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_carbon_organic) \n[iSDAsoil Organic Carbon](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_organic) \nOrganic carbon 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 ... \nISDASOIL/Africa/v1/carbon_organic, africa,carbon,isda,soil \n2001-01-01T00:00:00Z/2017-01-01T00:00:00Z \n-35.22 -31.46 37.98 57.08 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [](https://doi.org/https://isda-africa.com/)\n- [](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_organic)"]]
