iSDAsoil Bulk Density, <2mm Fraction
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/bulk_density")
open_in_new
태그
africa
isda
soil
bulk-density
대역
픽셀 크기
대역
이름
단위
최소
최대
픽셀 크기
설명
mean_0_20g/cm^3
44
197
미터
용적 밀도, 2mm 미만 비율, 0~20cm 깊이에서 예측된 평균
mean_20_50g/cm^3
44
196
미터
밀도, 2mm 미만 비율, 20~50cm 깊이에서 예측된 평균
stdev_0_20g/cm^3
0
92
미터
밀도, 2mm 미만 비율, 0~20cm 깊이의 표준 편차
stdev_20_50g/cm^3
0
92
미터
밀도, 2mm 미만 비율, 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:
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코드 편집기(JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>' +
'<ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>' +
'<ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>' +
'<ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>' +
'<ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>' +
'<ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>' +
'<ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>' +
'<ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>' +
'<ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>' +
'<ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>' +
'<ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>' +
'<ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>' +
'<ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>' +
'<ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>' +
'<ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>' +
'<ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>' +
'<ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>' +
'<ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>' +
'<ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>' +
'<ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>' +
'<ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>' +
'<ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>' +
'<ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>' +
'<ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>' +
'<ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>' +
'<ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>' +
'<ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>' +
'<ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>' +
'<ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/bulk_density" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Bulk density, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Bulk density, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Bulk density, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Bulk density, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 100 );
var visualization = { min : 1 , max : 1.5 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Bulk density, mean, 0-20 cm" );
코드 편집기에서 열기
[null,null,[],[[["\u003cp\u003eThis dataset provides soil bulk density data for Africa at 30-meter resolution, covering the period from 2001 to 2017.\u003c/p\u003e\n"],["\u003cp\u003eIt includes predicted mean and standard deviation of bulk density for soil depths of 0-20 cm and 20-50 cm.\u003c/p\u003e\n"],["\u003cp\u003eThe data is derived from machine learning models trained on over 100,000 soil samples and remote sensing data, with potential for lower accuracy in dense jungle areas.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require back-transformation by dividing by 100 to obtain the actual bulk density in g/cm³.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is provided by Innovative Solutions for Decision Agriculture Ltd.(iSDA) under a CC-BY-4.0 license.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Bulk Density, <2mm Fraction\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) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \nbulk-density \n\n#### Description\n\nBulk density, \\\u003c2mm fraction at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `x/100`.\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/cm\\^3 | 44 | 197 | meters | Bulk density, \\\u003c2mm fraction, predicted mean at 0-20 cm depth |\n| `mean_20_50` | g/cm\\^3 | 44 | 196 | meters | Bulk density, \\\u003c2mm fraction, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | g/cm\\^3 | 0 | 92 | meters | Bulk density, \\\u003c2mm fraction, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | g/cm\\^3 | 0 | 92 | meters | Bulk density, \\\u003c2mm fraction, 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=\"#00204D\" label=\"0.8-1.05\" opacity=\"1\" quantity=\"105\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"1.05-1.19\" opacity=\"1\" quantity=\"119\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"1.19-1.23\" opacity=\"1\" quantity=\"123\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"1.23-1.25\" opacity=\"1\" quantity=\"125\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"1.25-1.28\" opacity=\"1\" quantity=\"128\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"1.28-1.31\" opacity=\"1\" quantity=\"131\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"1.31-1.34\" opacity=\"1\" quantity=\"134\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"1.34-1.36\" opacity=\"1\" quantity=\"136\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"1.36-1.38\" opacity=\"1\" quantity=\"138\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"1.38-1.41\" opacity=\"1\" quantity=\"141\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"1.41-1.43\" opacity=\"1\" quantity=\"143\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"1.43-1.45\" opacity=\"1\" quantity=\"145\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"1.45-1.48\" opacity=\"1\" quantity=\"148\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"1.48-1.51\" opacity=\"1\" quantity=\"151\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"1.51-1.85\" opacity=\"1\" quantity=\"154\"/\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=\"#00204D\" label=\"0.8-1.05\" opacity=\"1\" quantity=\"105\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"1.05-1.19\" opacity=\"1\" quantity=\"119\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"1.19-1.23\" opacity=\"1\" quantity=\"123\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"1.23-1.25\" opacity=\"1\" quantity=\"125\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"1.25-1.28\" opacity=\"1\" quantity=\"128\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"1.28-1.31\" opacity=\"1\" quantity=\"131\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"1.31-1.34\" opacity=\"1\" quantity=\"134\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"1.34-1.36\" opacity=\"1\" quantity=\"136\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"1.36-1.38\" opacity=\"1\" quantity=\"138\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"1.38-1.41\" opacity=\"1\" quantity=\"141\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"1.41-1.43\" opacity=\"1\" quantity=\"143\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"1.43-1.45\" opacity=\"1\" quantity=\"145\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"1.45-1.48\" opacity=\"1\" quantity=\"148\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"1.48-1.51\" opacity=\"1\" quantity=\"151\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"1.51-1.85\" opacity=\"1\" quantity=\"154\"/\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=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"9\"/\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=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/bulk_density\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Bulk density, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Bulk density, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Bulk density, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Bulk density, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(100);\n\nvar visualization = {min: 1, max: 1.5};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Bulk density, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_bulk_density) \n[iSDAsoil Bulk Density, \\\u003c2mm Fraction](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_bulk_density) \nBulk density, \\\u003c2mm fraction at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with x/100. 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 ... \nISDASOIL/Africa/v1/bulk_density, africa,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_bulk_density)"]]
