عمق به سنگ بستر در عمق 0-200 سانتی متر، میانگین پیش بینی شده و انحراف معیار.
با توجه به ماسک بالقوه زمین های زراعی که برای تولید داده ها استفاده شد، بسیاری از مناطق سنگ در معرض (که عمق تا سنگ بستر 0 سانتی متر است) پوشانده شده اند و بنابراین به عنوان مقادیر گره ظاهر می شوند. حداکثر عمق این لایه 200 سانتی متر است، اما این حداکثر عمق ممکن خاک را نشان نمی دهد، بنابراین مقادیر 200 باید به صورت >= 200 تفسیر شود.
در مناطق جنگلی متراکم (به طور کلی بر فراز آفریقای مرکزی)، دقت مدل پایین است و بنابراین ممکن است مصنوعاتی مانند نواربندی (راه راه) دیده شود.
پیش‌بینی ویژگی‌های خاک توسط Innovative Solutions for Decision Agriculture Ltd. (iSDA) در اندازه پیکسل 30 متر با استفاده از یادگیری ماشین همراه با داده‌های سنجش از راه دور و مجموعه آموزشی بیش از 100000 نمونه خاک تجزیه‌شده انجام شد.
Hengl, T., Miller, MAE, Križan, J., et al. ویژگی‌های خاک آفریقا و مواد مغذی با وضوح فضایی 30 متر با استفاده از یادگیری ماشین دو مقیاسی نقشه‌برداری شدند. Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y
عمق به سنگ بستر در عمق 0-200 سانتی متر، میانگین پیش بینی شده و انحراف معیار. با توجه به ماسک بالقوه زمین های زراعی که برای تولید داده ها استفاده شد، بسیاری از مناطق سنگ در معرض (که عمق تا سنگ بستر 0 سانتی متر است) پوشانده شده اند و بنابراین به عنوان مقادیر گره ظاهر می شوند. حداکثر…
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of bedrock depth at 0-200 cm for Africa.\u003c/p\u003e\n"],["\u003cp\u003eIt was created by iSDA using machine learning and remote sensing data with over 100,000 soil samples.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset has a 30-meter resolution and covers the period from 2001 to 2017.\u003c/p\u003e\n"],["\u003cp\u003eAreas with exposed rock and dense jungles may have lower accuracy or missing data.\u003c/p\u003e\n"],["\u003cp\u003eThe data is available under the CC-BY-4.0 license.\u003c/p\u003e\n"]]],["The iSDA dataset provides predicted mean and standard deviation of bedrock depth in Africa, ranging from 0-200 cm, with a pixel size of 30 meters. Data spans from 2001-01-01 to 2017-01-01. It was created using machine learning and over 100,000 soil samples by Innovative Solutions for Decision Agriculture Ltd. (iSDA). Areas with exposed rock are masked, and dense jungle regions may show reduced model accuracy. The dataset can be accessed through Google Earth Engine.\n"],null,["# iSDAsoil Depth to Bedrock\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) [bedrock](/earth-engine/datasets/tags/bedrock) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\nDepth to bedrock at 0-200 cm depth, predicted mean and standard deviation.\n\nDue to the potential cropland mask that was used for generating the data,\nmany areas of exposed rock (where depth to bedrock would be 0 cm) have been\nmasked out and therefore appear as nodata values. The maximum depth of this\nlayer is 200 cm, but this does not represent the maximum possible soil depth,\ntherefore values of 200 should be interpreted as \\\u003e= 200.\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\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_200` | cm | 27 | 200 | meters | Depth to bedrock, predicted mean at 0-200 cm depth |\n| `stdev_0_200` | cm | 9 | 254 | meters | Depth to bedrock, standard deviation at 0-20 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_200 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#00204D\" label=\"0-13\" opacity=\"1\" quantity=\"13\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"13-26\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"26-39\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"39-52\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"52-65\" opacity=\"1\" quantity=\"65\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"65-78\" opacity=\"1\" quantity=\"78\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"78-91\" opacity=\"1\" quantity=\"91\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"91-104\" opacity=\"1\" quantity=\"104\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"104-117\" opacity=\"1\" quantity=\"117\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"117-130\" opacity=\"1\" quantity=\"130\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"130-143\" opacity=\"1\" quantity=\"143\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"143-156\" opacity=\"1\" quantity=\"156\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"156-169\" opacity=\"1\" quantity=\"169\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"169-182\" opacity=\"1\" quantity=\"182\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"182-200\" opacity=\"1\" quantity=\"195\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar stdev_0_200 =\n'\u003cRasterSymbolizer\u003e' +\n '\u003cColorMap type=\"ramp\"\u003e' +\n '\u003cColorMapEntry color=\"#fde725\" label=\"low\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/bedrock_depth\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_200), {},\n \"Bedrock depth, mean visualization, 0-200 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(stdev_0_200), {},\n \"Bedrock depth, stdev visualization, 0-200 cm\");\n\nvar visualization = {min: 27, max: 200};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(raw.select(0), visualization, \"Bedrock depth, mean, 0-200 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_bedrock_depth) \n[iSDAsoil Depth to Bedrock](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_bedrock_depth) \nDepth to bedrock at 0-200 cm depth, predicted mean and standard deviation. Due to the potential cropland mask that was used for generating the data, many areas of exposed rock (where depth to bedrock would be 0 cm) have been masked out and therefore appear as nodata values. The maximum ... \nISDASOIL/Africa/v1/bedrock_depth, africa,bedrock,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_bedrock_depth)"]]
