Содержание камней на глубине почвы 0-20 см и 20-50 см, прогнозируемое среднее значение и стандартное отклонение.
Значения пикселей должны быть обратно преобразованы с помощью exp(x/10)-1 .
В районах густых джунглей (как правило, в Центральной Африке) точность модели низкая, поэтому могут быть видны такие артефакты, как полосатость.
Прогнозы свойств почвы были сделаны компанией Innovative Solutions for Decision Agriculture Ltd. (iSDA) с размером пикселя 30 м с использованием машинного обучения в сочетании с данными дистанционного зондирования и обучающим набором из более чем 100 000 проанализированных образцов почвы.
Хенгль, Т., Миллер, М.А.Э., Крижан, Дж. и др. Свойства и питательные вещества африканских почв, картированные с пространственным разрешением 30 м с использованием двухмасштабного ансамблевого машинного обучения. Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y
Содержание камней на глубине 0–20 см и 20–50 см, прогнозируемое среднее значение и стандартное отклонение. Значения пикселей необходимо преобразовать обратно с помощью exp(x/10)-1. В районах густых джунглей (обычно в Центральной Африке) точность модели низкая, поэтому могут наблюдаться артефакты, такие как полосчатость. Прогнозы свойств почвы были…
[null,null,[],[[["\u003cp\u003eThis dataset provides predictions of stone content 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\u003eData covers the African continent from 2001 to 2017 at a 30-meter resolution, created by iSDA using machine learning and remote sensing.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require back-transformation using a specific formula (\u003ccode\u003eexp(x/10)-1\u003c/code\u003e) to obtain the actual stone content percentage.\u003c/p\u003e\n"],["\u003cp\u003eAccuracy may be lower in dense jungle regions of central Africa, potentially leading to visual artifacts like banding.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is licensed under CC-BY-4.0 and users should refer to the provided citation for proper attribution.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Stone 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) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \nstone \n\n#### Description\n\nStone content 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\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 | 42 | meters | Stone content, predicted mean at 0-20 cm depth |\n| `mean_20_50` | % | 0 | 42 | meters | Stone content, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | % | 1 | 159 | meters | Stone content, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | % | 1 | 158 | meters | Stone 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=\"0-0.1\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"0.1-0.3\" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"0.3-0.5\" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"0.5-0.6\" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"0.6-0.8\" opacity=\"1\" quantity=\"6\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"0.8-1\" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"1-1.2\" opacity=\"1\" quantity=\"8\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"1.2-1.5\" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"1.5-1.7\" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"1.7-2\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"2-2.3\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"2.3-2.7\" opacity=\"1\" quantity=\"13\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"2.7-3.1\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"3.1-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"3.5-80\" opacity=\"1\" quantity=\"16\"/\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=\"0-0.1\" opacity=\"1\" quantity=\"1\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"0.1-0.3\" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"0.3-0.5\" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"0.5-0.6\" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"0.6-0.8\" opacity=\"1\" quantity=\"6\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"0.8-1\" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"1-1.2\" opacity=\"1\" quantity=\"8\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"1.2-1.5\" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"1.5-1.7\" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"1.7-2\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"2-2.3\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"2.3-2.7\" opacity=\"1\" quantity=\"13\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"2.7-3.1\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"3.1-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"3.5-80\" opacity=\"1\" quantity=\"16\"/\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=\"5\"/\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=\"5\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/stone_content\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Stone content, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Stone content, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Stone content, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Stone content, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 6};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Stone content, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_stone_content) \n[iSDAsoil Stone Content](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_stone_content) \nStone content 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. 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 ... \nISDASOIL/Africa/v1/stone_content, africa,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_stone_content)"]]
