iSDAsoil Total Carbon
컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
데이터 세트 사용 가능 기간
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
데이터 세트 제공업체
iSDA
Earth Engine 스니펫
ee.Image("ISDASOIL/Africa/v1/carbon_total")
open_in_new
태그
아프리카
알루미늄
ISDA
토양
대역
픽셀 크기
30미터
대역
이름
단위
최소
최대
픽셀 크기
설명
mean_0_20
g/kg
0
58
미터
탄소, 총량, 0~20cm 깊이에서 예측된 평균
mean_20_50
g/kg
0
55
미터
탄소, 총계, 20~50cm 깊이의 예측 평균
stdev_0_20
g/kg
0
151
미터
탄소, 총계, 0~20cm 깊이의 표준 편차
stdev_20_50
g/kg
0
150
미터
탄소, 총량, 20~50cm 깊이에서의 표준 편차
인용
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 Engine으로 탐색하기
중요:
Earth Engine은 공익을 위해 그리고 비즈니스 및 정부 사용자를 위해 페타바이트 규모의 과학적 분석 및 지리 공간 데이터 세트 시각화를 위한 플랫폼입니다.
Earth Engine은 연구, 교육, 비영리 목적으로 무료로 사용할 수 있습니다. 시작하려면 Earth Engine 액세스를 등록 하세요.
코드 편집기(JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' +
'<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' +
'<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' +
'<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' +
'<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' +
'<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' +
'<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' +
'<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' +
'<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' +
'<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' +
'<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' +
'</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="3"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>' +
'<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="3"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/carbon_total" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Carbon, total, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Carbon, total, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Carbon, total, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Carbon, total, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 10 ). exp (). subtract ( 1 );
var visualization = { min : 0 , max : 60 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Carbon, total, mean, 0-20 cm" );
코드 편집기에서 열기
[null,null,[],[[["\u003cp\u003eThe ISDASOIL/Africa/v1/carbon_total dataset provides predictions for total carbon content in African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eData includes predicted mean and standard deviation of total carbon, requiring back-transformation using \u003ccode\u003eexp(x/10)-1\u003c/code\u003e for actual values.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset covers the period from 2001 to 2017 and has a 30-meter resolution.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is potentially lower in dense jungle areas, leading to possible artifacts like banding.\u003c/p\u003e\n"],["\u003cp\u003eiSDA developed the dataset using machine learning and remote sensing data, coupled with extensive soil sample analysis.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Total 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) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\nTotal 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 | 0 | 58 | meters | Carbon, total, predicted mean at 0-20 cm depth |\n| `mean_20_50` | g/kg | 0 | 55 | meters | Carbon, total, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | g/kg | 0 | 151 | meters | Carbon, total, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | g/kg | 0 | 150 | meters | Carbon, total, 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\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"5.7-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"10-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"19.1-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"21.2-23.5\" opacity=\"1\" quantity=\"32\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"23.5-26.1\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"26.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"29-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"32.1-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"35.6-40\" opacity=\"1\" quantity=\"39\"/\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\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"5.7-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"10-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"19.1-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"21.2-23.5\" opacity=\"1\" quantity=\"32\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"23.5-26.1\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"26.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"29-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"32.1-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"35.6-40\" opacity=\"1\" quantity=\"39\"/\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=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"6\"/\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=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"6\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/carbon_total\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Carbon, total, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Carbon, total, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Carbon, total, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Carbon, total, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 60};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Carbon, total, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_carbon_total) \n[iSDAsoil Total Carbon](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_total) \nTotal 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_total, africa,aluminium,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_total)"]]