iSDAsoil Organic Carbon
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
資料集可用性
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
資料集來源
iSDA
Earth Engine 程式碼片段
ee.Image("ISDASOIL/Africa/v1/carbon_organic")
open_in_new
標記
africa
carbon
isda
soil
carbon-organic
頻帶
像素大小
30 公尺
頻帶
名稱
單位
最小值
最大值
像素大小
說明
mean_0_20
g/kg
1
46
公尺
碳、有機、預測平均值 (深度 0 到 20 公分)
mean_20_50
g/kg
0
46
公尺
碳、有機、預測平均值 (20-50 公分深度)
stdev_0_20
g/kg
0
12
公尺
0 到 20 公分深度的碳、有機物和標準差
stdev_20_50
g/kg
0
13
公尺
20 到 50 公分深度的碳、有機物標準差
引用內容
Hengl, T.、Miller, M.A.E.、Križan, J. 等人。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
使用 Earth Engine 探索
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程式碼編輯器 (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" );
在程式碼編輯器中開啟
[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)"]]