iSDAsoil Extractable Calcium
コレクションでコンテンツを整理
必要に応じて、コンテンツの保存と分類を行います。
- データセットの可用性
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- データセット プロバイダ
-
iSDA
- Earth Engine スニペット
-
ee.Image("ISDASOIL/Africa/v1/calcium_extractable")
open_in_new
- タグ
-
africa
isda
soil
カルシウム
バンド
ピクセルサイズ
30 メートル
帯域
| 名前 |
単位 |
最小 |
最大 |
ピクセルサイズ |
説明 |
mean_0_20 |
ppm |
20
|
100
|
メートル
|
カルシウム（抽出可能）、0 ～ 20 cm の深さでの予測平均値 |
mean_20_50 |
ppm |
14
|
100
|
メートル
|
カルシウム（抽出可能）、予測平均値（深さ 20 ～ 50 cm） |
stdev_0_20 |
ppm |
0
|
62
|
メートル
|
カルシウム、抽出可能、0 ～ 20 cm の深さでの標準偏差 |
stdev_20_50 |
ppm |
0
|
63
|
メートル
|
カルシウム、抽出可能、20 ～ 50 cm の深さでの標準偏差 |
引用
Hengl, T.、Miller, M.A.E.、Križan, J. 他。2 つのスケールのアンサンブル機械学習を使用して 30 m の空間分解能でマッピングされたアフリカの土壌特性と栄養素。Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
Earth Engine で探索する
コードエディタ（JavaScript）
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-65.7" opacity="1" quantity="42"/>' +
'<ColorMapEntry color="#350498" label="65.7-120.5" opacity="1" quantity="48"/>' +
'<ColorMapEntry color="#5402A3" label="120.5-163" opacity="1" quantity="51"/>' +
'<ColorMapEntry color="#7000A8" label="163-199.3" opacity="1" quantity="53"/>' +
'<ColorMapEntry color="#8B0AA5" label="199.3-269.4" opacity="1" quantity="56"/>' +
'<ColorMapEntry color="#A31E9A" label="269.4-329.3" opacity="1" quantity="58"/>' +
'<ColorMapEntry color="#B93289" label="329.3-402.4" opacity="1" quantity="60"/>' +
'<ColorMapEntry color="#CC4678" label="402.4-491.7" opacity="1" quantity="62"/>' +
'<ColorMapEntry color="#DB5C68" label="491.7-600.8" opacity="1" quantity="64"/>' +
'<ColorMapEntry color="#E97158" label="600.8-664.1" opacity="1" quantity="65"/>' +
'<ColorMapEntry color="#F48849" label="664.1-811.4" opacity="1" quantity="67"/>' +
'<ColorMapEntry color="#FBA139" label="811.4-896.8" opacity="1" quantity="68"/>' +
'<ColorMapEntry color="#FEBC2A" label="896.8-1095.6" opacity="1" quantity="70"/>' +
'<ColorMapEntry color="#FADA24" label="1095.6-1479.3" opacity="1" quantity="73"/>' +
'<ColorMapEntry color="#F0F921" label="1479.3-12000" opacity="1" quantity="77"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-65.7" opacity="1" quantity="42"/>' +
'<ColorMapEntry color="#350498" label="65.7-120.5" opacity="1" quantity="48"/>' +
'<ColorMapEntry color="#5402A3" label="120.5-163" opacity="1" quantity="51"/>' +
'<ColorMapEntry color="#7000A8" label="163-199.3" opacity="1" quantity="53"/>' +
'<ColorMapEntry color="#8B0AA5" label="199.3-269.4" opacity="1" quantity="56"/>' +
'<ColorMapEntry color="#A31E9A" label="269.4-329.3" opacity="1" quantity="58"/>' +
'<ColorMapEntry color="#B93289" label="329.3-402.4" opacity="1" quantity="60"/>' +
'<ColorMapEntry color="#CC4678" label="402.4-491.7" opacity="1" quantity="62"/>' +
'<ColorMapEntry color="#DB5C68" label="491.7-600.8" opacity="1" quantity="64"/>' +
'<ColorMapEntry color="#E97158" label="600.8-664.1" opacity="1" quantity="65"/>' +
'<ColorMapEntry color="#F48849" label="664.1-811.4" opacity="1" quantity="67"/>' +
'<ColorMapEntry color="#FBA139" label="811.4-896.8" opacity="1" quantity="68"/>' +
'<ColorMapEntry color="#FEBC2A" label="896.8-1095.6" opacity="1" quantity="70"/>' +
'<ColorMapEntry color="#FADA24" label="1095.6-1479.3" opacity="1" quantity="73"/>' +
'<ColorMapEntry color="#F0F921" label="1479.3-12000" opacity="1" quantity="77"/>' +
'</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/calcium_extractable");
Map.addLayer(
raw.select(0).sldStyle(mean_0_20), {},
"Calcium, extractable, mean visualization, 0-20 cm");
Map.addLayer(
raw.select(1).sldStyle(mean_20_50), {},
"Calcium, extractable, mean visualization, 20-50 cm");
Map.addLayer(
raw.select(2).sldStyle(stdev_0_20), {},
"Calcium, extractable, stdev visualization, 0-20 cm");
Map.addLayer(
raw.select(3).sldStyle(stdev_20_50), {},
"Calcium, extractable, stdev visualization, 20-50 cm");
var converted = raw.divide(10).exp().subtract(1);
var visualization = {min: 100, max: 2000};
Map.setCenter(25, -3, 2);
Map.addLayer(converted.select(0), visualization, "Calcium, extractable, mean, 0-20 cm");
コードエディタで開く
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of extractable calcium in African soil at two depths (0-20 cm and 20-50 cm).\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.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require back-transformation using the formula \u003ccode\u003eexp(x/10)-1\u003c/code\u003e to obtain actual calcium concentrations in ppm.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset has a 30-meter resolution and may exhibit lower accuracy in dense jungle regions of central Africa.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access this dataset through Google Earth Engine and are encouraged to consult the provided resources for detailed information and support.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Extractable Calcium\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) \ncalcium \n\n#### Description\n\nExtractable calcium 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` | ppm | 20 | 100 | meters | Calcium, extractable, predicted mean at 0-20 cm depth |\n| `mean_20_50` | ppm | 14 | 100 | meters | Calcium, extractable, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | ppm | 0 | 62 | meters | Calcium, extractable, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | ppm | 0 | 63 | meters | Calcium, extractable, 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=\"#0D0887\" label=\"0-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"65.7-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"120.5-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"163-199.3\" opacity=\"1\" quantity=\"53\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"199.3-269.4\" opacity=\"1\" quantity=\"56\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"269.4-329.3\" opacity=\"1\" quantity=\"58\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"329.3-402.4\" opacity=\"1\" quantity=\"60\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"402.4-491.7\" opacity=\"1\" quantity=\"62\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"491.7-600.8\" opacity=\"1\" quantity=\"64\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"600.8-664.1\" opacity=\"1\" quantity=\"65\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"664.1-811.4\" opacity=\"1\" quantity=\"67\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"811.4-896.8\" opacity=\"1\" quantity=\"68\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"896.8-1095.6\" opacity=\"1\" quantity=\"70\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"1095.6-1479.3\" opacity=\"1\" quantity=\"73\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"1479.3-12000\" opacity=\"1\" quantity=\"77\"/\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=\"#0D0887\" label=\"0-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"65.7-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"120.5-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"163-199.3\" opacity=\"1\" quantity=\"53\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"199.3-269.4\" opacity=\"1\" quantity=\"56\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"269.4-329.3\" opacity=\"1\" quantity=\"58\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"329.3-402.4\" opacity=\"1\" quantity=\"60\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"402.4-491.7\" opacity=\"1\" quantity=\"62\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"491.7-600.8\" opacity=\"1\" quantity=\"64\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"600.8-664.1\" opacity=\"1\" quantity=\"65\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"664.1-811.4\" opacity=\"1\" quantity=\"67\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"811.4-896.8\" opacity=\"1\" quantity=\"68\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"896.8-1095.6\" opacity=\"1\" quantity=\"70\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"1095.6-1479.3\" opacity=\"1\" quantity=\"73\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"1479.3-12000\" opacity=\"1\" quantity=\"77\"/\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/calcium_extractable\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Calcium, extractable, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Calcium, extractable, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Calcium, extractable, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Calcium, extractable, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 100, max: 2000};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Calcium, extractable, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_calcium_extractable) \n[iSDAsoil Extractable Calcium](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_calcium_extractable) \nExtractable calcium 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/calcium_extractable, 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_calcium_extractable)"]]
