iSDAsoil extractable Aluminium
Sử dụng bộ sưu tập để sắp xếp ngăn nắp các trang
Lưu và phân loại nội dung dựa trên lựa chọn ưu tiên của bạn.
Phạm vi cung cấp tập dữ liệu
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Nhà cung cấp tập dữ liệu
iSDA
Đoạn mã Earth Engine
ee.Image("ISDASOIL/Africa/v1/aluminium_extractable")
open_in_new
Thẻ
africa
aluminium
isda
soil
Mô tả
Nhôm có thể chiết xuất ở độ sâu 0-20 cm và 20-50 cm của đất, giá trị trung bình và độ lệch chuẩn dự đoán.
Bạn phải chuyển đổi ngược các giá trị pixel bằng exp(x/10)-1.
Công ty Innovative Solutions for Decision Agriculture Ltd. (iSDA) đã đưa ra dự đoán về đặc tính của đất ở kích thước pixel 30 m bằng cách sử dụng công nghệ học máy kết hợp với dữ liệu viễn thám và một bộ dữ liệu huấn luyện gồm hơn 100.000 mẫu đất đã phân tích.
Bạn có thể xem thêm thông tin trong Câu hỏi thường gặp và tài liệu về thông tin kỹ thuật . Để gửi vấn đề hoặc yêu cầu hỗ trợ, vui lòng truy cập trang web iSDAsoil .
Ở những khu vực có rừng rậm (thường là ở Trung Phi), độ chính xác của mô hình thấp và do đó, bạn có thể thấy các hiện tượng như dải màu (vệt sọc).
Băng tần
Kích thước pixel
Băng tần
Tên
Đơn vị
Tối thiểu
Tối đa
Kích thước pixel
Mô tả
mean_0_20ppm
3
80
mét
Nhôm, có thể chiết xuất, giá trị trung bình dự đoán ở độ sâu 0-20 cm
mean_20_50ppm
4
79
mét
Nhôm, có thể chiết xuất, giá trị trung bình dự đoán ở độ sâu 20-50 cm
stdev_0_20ppm
1
53
mét
Nhôm, có thể chiết xuất, độ lệch chuẩn ở độ sâu 0-20 cm
stdev_20_50ppm
1
51
mét
Nhôm, có thể chiết xuất, độ lệch chuẩn ở độ sâu 20-50 cm
Điều khoản sử dụng
Điều khoản sử dụng
CC-BY-4.0
Trích dẫn
Hengl, T., Miller, M.A.E., Križan, J., và cộng sự. Các thuộc tính và chất dinh dưỡng của đất ở Châu Phi được lập bản đồ ở độ phân giải không gian 30 m bằng cách sử dụng mô hình học máy kết hợp hai tỷ lệ.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
Hengl, T., Miller, M.A.E., Križan, J., và cộng sự. Các thuộc tính và chất dinh dưỡng của đất ở Châu Phi được lập bản đồ ở độ phân giải không gian 30 m bằng cách sử dụng mô hình học máy kết hợp hai tỷ lệ.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
Khám phá bằng Earth Engine
Lưu ý quan trọng:
Earth Engine là một nền tảng để phân tích khoa học và trực quan hoá các tập dữ liệu không gian địa lý ở quy mô petabyte, vừa mang lại lợi ích cho cộng đồng, vừa phục vụ người dùng doanh nghiệp và chính phủ.
Bạn có thể sử dụng Earth Engine miễn phí cho mục đích nghiên cứu, giáo dục và phi lợi nhuận. Để bắt đầu, vui lòng đăng ký quyền truy cập vào Earth Engine .
Trình soạn thảo mã (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" );
Mở trong Trình soạn thảo mã
[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)"]]
