Teneur en argile à des profondeurs de sol de 0 à 20 cm et de 20 à 50 cm, moyenne et écart-type prévus.
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.
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.
Teneur en argile à des profondeurs de sol de 0 à 20 cm et de 20 à 50 cm, moyenne et écart-type prévus. 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. Les prédictions sur les propriétés du sol ont été effectuées par Innovative Solutions for Decision Agriculture Ltd. …
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of clay content in African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eThe data covers the African continent from 2001 to 2017 at a 30-meter resolution.\u003c/p\u003e\n"],["\u003cp\u003ePredictions were generated by iSDA using machine learning, remote sensing data, and over 100,000 soil samples.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is reduced in dense jungle areas, potentially leading to visual artifacts.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is licensed under CC-BY-4.0 and is available for use in Google Earth Engine.\u003c/p\u003e\n"]]],["The dataset, provided by iSDA, offers clay content predictions for African soils from 2001 to 2017. Using machine learning and remote sensing, data was collected for soil depths of 0-20 cm and 20-50 cm. The data include predicted mean and standard deviation values, available in four bands with a 30-meter pixel size. The data also provides an Earth Engine snippet for direct access and visualization, under the terms of CC-BY-4.0.\n"],null,["# iSDAsoil Clay Content\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) [clay](/earth-engine/datasets/tags/clay) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\nClay content at soil depths of 0-20 cm and 20-50 cm,\\\\npredicted mean and standard deviation.\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` | % | 0 | 84 | meters | Clay content, predicted mean at 0-20 cm depth |\n| `mean_20_50` | % | 0 | 78 | meters | Clay content, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | % | 0 | 90 | meters | Clay content, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | % | 0 | 90 | meters | Clay content, 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=\"#00204D\" label=\"0-8\" opacity=\"1\" quantity=\"8\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"8-14\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"14-17\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"17-19\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"19-21\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"21-22\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"22-24\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"24-25\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"25-26\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"26-28\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"28-30\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"30-31\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"31-33\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"33-36\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"36-70\" opacity=\"1\" quantity=\"40\"/\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=\"#00204D\" label=\"0-8\" opacity=\"1\" quantity=\"8\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"8-14\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"14-17\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"17-19\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"19-21\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"21-22\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"22-24\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"24-25\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"25-26\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"26-28\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"28-30\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"30-31\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"31-33\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"33-36\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"36-70\" opacity=\"1\" quantity=\"40\"/\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=\"6\"/\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=\"6\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/clay_content\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Clay content, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Clay content, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Clay content, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Clay content, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 50};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Clay content, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_clay_content) \n[iSDAsoil Clay Content](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_clay_content) \nClay content at soil depths of 0-20 cm and 20-50 cm,\\\\npredicted mean and standard deviation. 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 made by Innovative Solutions for Decision Agriculture Ltd. ... \nISDASOIL/Africa/v1/clay_content, africa,clay,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_clay_content)"]]
