iSDAsoil extractable Aluminium
จัดทุกอย่างให้เป็นระเบียบอยู่เสมอด้วยคอลเล็กชัน
บันทึกและจัดหมวดหมู่เนื้อหาตามค่ากำหนดของคุณ
- ความพร้อมใช้งานของชุดข้อมูล
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- ผู้ให้บริการชุดข้อมูล
-
iSDA
- ตัวอย่างข้อมูล Earth Engine
-
ee.Image("ISDASOIL/Africa/v1/aluminium_extractable")
open_in_new
- แท็ก
-
africa
aluminium
isda
soil
คำอธิบาย
อลูมิเนียมที่สกัดได้ที่ความลึกของดิน 0-20 ซม. และ 20-50 ซม.
ค่าเฉลี่ยที่คาดการณ์ไว้และค่าเบี่ยงเบนมาตรฐาน
ต้องแปลงค่าพิกเซลกลับด้วย exp(x/10)-1
การคาดการณ์คุณสมบัติของดินดำเนินการโดย
Innovative Solutions for Decision Agriculture Ltd. (iSDA)
ที่ขนาดพิกเซล 30 ม. โดยใช้แมชชีนเลิร์นนิงร่วมกับข้อมูลการรับรู้จากระยะไกล
และชุดการฝึกที่มีตัวอย่างดินที่วิเคราะห์แล้วกว่า 100,000 ตัวอย่าง
ดูข้อมูลเพิ่มเติมได้ในคำถามที่พบบ่อยและเอกสารประกอบเกี่ยวกับข้อมูลทางเทคนิค หากต้องการส่งปัญหาหรือขอรับการสนับสนุน โปรดไปที่เว็บไซต์ iSDAsoil
ในพื้นที่ป่าทึบ (โดยทั่วไปคือแอฟริกากลาง) ความแม่นยำของโมเดลจะต่ำ จึงอาจเห็นสิ่งประดิษฐ์ เช่น แถบ (ลาย)
ย่านความถี่
ขนาดพิกเซล
30 เมตร
ย่านความถี่
| ชื่อ |
หน่วย |
ต่ำสุด |
สูงสุด |
ขนาดพิกเซล |
คำอธิบาย |
mean_0_20 |
ppm |
3
|
80
|
เมตร
|
อลูมิเนียมที่สกัดได้ ค่าเฉลี่ยที่คาดการณ์ไว้ที่ความลึก 0-20 ซม. |
mean_20_50 |
ppm |
4
|
79
|
เมตร
|
อลูมิเนียม สกัดได้ ค่าเฉลี่ยที่คาดการณ์ที่ความลึก 20-50 ซม. |
stdev_0_20 |
ppm |
1
|
53
|
เมตร
|
อลูมิเนียม สกัดได้ ค่าเบี่ยงเบนมาตรฐานที่ความลึก 0-20 ซม. |
stdev_20_50 |
ppm |
1
|
51
|
เมตร
|
อลูมิเนียมที่สกัดได้ ค่าเบี่ยงเบนมาตรฐานที่ความลึก 20-50 ซม. |
ข้อกำหนดในการใช้งาน
ข้อกำหนดในการใช้งาน
CC-BY-4.0
การอ้างอิง
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
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
สำรวจด้วย Earth Engine
ตัวแก้ไขโค้ด (JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' +
'<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' +
'<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' +
'<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' +
'<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' +
'<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' +
'<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' +
'<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' +
'<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' +
'<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' +
'<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' +
'<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' +
'<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' +
'<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' +
'<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' +
'<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' +
'<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' +
'<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' +
'<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' +
'<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' +
'<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' +
'<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' +
'<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' +
'<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' +
'<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';
var stdev_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';
var stdev_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';
Map.setCenter(25, -3, 2);
var raw = ee.Image("ISDASOIL/Africa/v1/aluminium_extractable");
Map.addLayer(
raw.select(0).sldStyle(mean_0_20), {},
"Aluminium, extractable, mean visualization, 0-20 cm");
Map.addLayer(
raw.select(1).sldStyle(mean_20_50), {},
"Aluminium, extractable, mean visualization, 20-50 cm");
Map.addLayer(
raw.select(2).sldStyle(stdev_0_20), {},
"Aluminium, extractable, stdev visualization, 0-20 cm");
Map.addLayer(
raw.select(3).sldStyle(stdev_20_50), {},
"Aluminium, extractable, stdev visualization, 20-50 cm");
var converted = raw.divide(10).exp().subtract(1);
Map.addLayer(
converted.select(0), {min: 0, max: 100},
"Aluminium, extractable, mean, 0-20 cm");
เปิดในตัวแก้ไขโค้ด
[null,null,[],[[["\u003cp\u003eThis dataset provides predictions for extractable aluminum in African soil at two depths (0-20 cm and 20-50 cm), including both mean and standard deviation values.\u003c/p\u003e\n"],["\u003cp\u003eThe data covers the period from 2001 to 2017 and was produced by Innovative Solutions for Decision Agriculture Ltd.(iSDA) using machine learning and remote sensing techniques.\u003c/p\u003e\n"],["\u003cp\u003ePixel values are initially transformed and require back-transformation using the formula \u003ccode\u003eexp(x/10)-1\u003c/code\u003e to obtain actual extractable aluminum values in ppm (parts per million).\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available at a 30-meter resolution and can be accessed and analyzed using Google Earth Engine.\u003c/p\u003e\n"],["\u003cp\u003eUsers should be aware that model accuracy is lower in dense jungle areas, potentially leading to visual artifacts like banding.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil extractable Aluminium\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\nExtractable aluminium 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\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\nIn areas of dense jungle (generally over central Africa), model accuracy is\nlow and therefore artifacts such as banding (striping) might be seen.\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 | 3 | 80 | meters | Aluminium, extractable, predicted mean at 0-20 cm depth |\n| `mean_20_50` | ppm | 4 | 79 | meters | Aluminium, extractable, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | ppm | 1 | 53 | meters | Aluminium, extractable, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | ppm | 1 | 51 | meters | Aluminium, 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- 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### DOIs\n\n- \u003chttps://doi.org/10.1038/s41598-021-85639-y\u003e\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=\"0-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"21.2-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"35.6-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"53.6-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"65.7-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"72.7-80.5\" opacity=\"1\" quantity=\"44\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"80.5-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"89-98.5\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"98.5-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"108.9-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"120.5-133.3\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"133.3-147.4\" opacity=\"1\" quantity=\"50\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"147.4-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"163-199.3\" opacity=\"1\" quantity=\"53\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"199.3-1800\" opacity=\"1\" quantity=\"55\"/\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=\"0-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"21.2-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"35.6-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"53.6-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"65.7-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"72.7-80.5\" opacity=\"1\" quantity=\"44\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"80.5-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"89-98.5\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"98.5-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"108.9-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"120.5-133.3\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"133.3-147.4\" opacity=\"1\" quantity=\"50\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"147.4-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"163-199.3\" opacity=\"1\" quantity=\"53\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"199.3-1800\" opacity=\"1\" quantity=\"55\"/\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=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"14\"/\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=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nMap.setCenter(25, -3, 2);\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/aluminium_extractable\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Aluminium, extractable, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Aluminium, extractable, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Aluminium, extractable, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Aluminium, extractable, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\nMap.addLayer(\n converted.select(0), {min: 0, max: 100},\n \"Aluminium, extractable, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_aluminium_extractable) \n[iSDAsoil extractable Aluminium](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_aluminium_extractable) \nExtractable aluminium 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. Soil property predictions were made by Innovative Solutions for Decision Agriculture Ltd. (iSDA) at 30 m pixel size using machine learning coupled with remote sensing data and ... \nISDASOIL/Africa/v1/aluminium_extractable, 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/10.1038/s41598-021-85639-y](https://doi.org/https://isda-africa.com/)\n- [https://doi.org/10.1038/s41598-021-85639-y](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_aluminium_extractable)"]]
