Extractable 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 a training set of over 100,000 analyzed soil samples.
Extractable 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 …
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[[["\u003cp\u003eThis dataset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,["Dataset 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\nDescription \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\nBands\n\n\n**Pixel Size**\n\n30 meters\n\n**Bands**\n\n| Name | Units | Min | Max | Description |\n|---------------|-------|-----|-----|--------------------------------------------------------------|\n| `mean_0_20` | ppm | 3 | 80 | Aluminium, extractable, predicted mean at 0-20 cm depth |\n| `mean_20_50` | ppm | 4 | 79 | Aluminium, extractable, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | ppm | 1 | 53 | Aluminium, extractable, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | ppm | 1 | 51 | Aluminium, extractable, standard deviation at 20-50 cm depth |\n\nTerms of Use\n\n**Terms of Use**\n\n[CC-BY-4.0](https://spdx.org/licenses/CC-BY-4.0.html)\n\nCitations \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\nDOIs\n\n- \u003chttps://doi.org/10.1038/s41598-021-85639-y\u003e\n\nExplore with Earth Engine **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\nCode 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)"]]
