iSDAsoil extractable Aluminium
Restez organisé à l'aide des collections
Enregistrez et classez les contenus selon vos préférences.
Disponibilité des ensembles de données
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Fournisseur de l'ensemble de données
iSDA
Extrait Earth Engine
ee.Image("ISDASOIL/Africa/v1/aluminium_extractable")
open_in_new
Tags
africa
aluminium
isda
soil
Description
Aluminium extractible à des profondeurs de sol de 0 à 20 cm et de 20 à 50 cm, moyenne et écart-type prévus.
Les valeurs de pixels doivent être retransformées avec exp(x/10)-1.
Les prédictions des propriétés du sol ont été effectuées par Innovative Solutions for Decision Agriculture Ltd. (iSDA) à une taille de pixel de 30 mètres à l'aide du machine learning associé à des données de télédétection et d'un ensemble d'entraînement de plus de 100 000 échantillons de sol analysés.
Pour en savoir plus, consultez les questions fréquentes et la documentation sur les informations techniques . Pour signaler un problème ou demander de l'aide, veuillez consulter le site iSDAsoil .
Dans les zones de jungle dense (généralement en Afrique centrale), la précision du modèle est faible. Des artefacts tels que des bandes (rayures) peuvent donc apparaître.
Bracelets
Taille des pixels
Bandes de fréquences
Nom
Unités
Min
Max
Taille des pixels
Description
mean_0_20ppm
3
80
mètres
Aluminium extractible, moyenne prévue à une profondeur de 0 à 20 cm
mean_20_50ppm
4
79
mètres
Aluminium extractible, moyenne prédite à une profondeur de 20 à 50 cm
stdev_0_20ppm
1
53
mètres
Aluminium extractible, écart-type à une profondeur de 0 à 20 cm
stdev_20_50ppm
1
51
mètres
Aluminium extractible, écart-type à une profondeur de 20 à 50 cm
Conditions d'utilisation
Conditions d'utilisation
CC-BY-4.0
Citations
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
Explorer avec Earth Engine
Important : Earth Engine est une plate-forme d'analyse et de visualisation scientifiques à l'échelle du pétaoctet pour les ensembles de données géospatiales, à la fois pour le bien public et pour les utilisateurs professionnels et gouvernementaux.
Earth Engine est un outil sans frais pour la recherche, l'enseignement et les organisations à but non lucratif. Pour commencer, veuillez demander l'accès à Earth Engine .
Éditeur de code (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" );
Ouvrir dans l'éditeur de code
[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)"]]
