pH در عمق 0-20 سانتی متر و 20-50 سانتی متر، میانگین و انحراف معیار پیش بینی شده است.
مقادیر پیکسل باید با x/10 به عقب تبدیل شوند.
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
pH در عمق 0-20 سانتی متر و 20-50 سانتی متر، میانگین و انحراف معیار پیش بینی شده است. مقادیر پیکسل باید با x/10 به عقب تبدیل شوند. 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 خواص خاک انجام شد…
[null,null,[],[[["\u003cp\u003eThe dataset provides soil pH data for Africa at two depths (0-20 cm and 20-50 cm), including predicted mean and standard deviation values.\u003c/p\u003e\n"],["\u003cp\u003eIt covers the period from 2001 to 2017 and has a spatial resolution of 30 meters.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require a back-transformation using the formula \u003ccode\u003ex/10\u003c/code\u003e for accurate pH representation.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is provided by iSDA and is available under the CC-BY-4.0 license.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy may be lower in dense jungle regions, potentially leading to visual artifacts.\u003c/p\u003e\n"]]],["The iSDA provides a soil pH dataset for Africa, spanning 2001-2017. It includes predicted mean and standard deviation at 0-20 cm and 20-50 cm depths, with a 30-meter pixel size. Data is from iSDA, derived via machine learning and remote sensing, based on over 100,000 soil samples. Pixel values require back-transformation (x/10), and model accuracy may be low in dense jungle areas. The data can be accessed in the Google Earth Engine using the provided snippet.\n"],null,["# iSDAsoil pH\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) [ph](/earth-engine/datasets/tags/ph) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\npH at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `x/10`.\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 | Min | Max | Pixel Size | Description |\n|---------------|-----|-----|------------|------------------------------------------|\n| `mean_0_20` | 35 | 103 | meters | pH, predicted mean at 0-20 cm depth |\n| `mean_20_50` | 35 | 102 | meters | pH, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | 0 | 18 | meters | pH, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | 0 | 18 | meters | pH, 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=\"#CC0000\" label=\"3.5-4.6\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#FF0000\" label=\"4.6-4.9\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#FF5500\" label=\"4.9-5.2\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFAA00\" label=\"5.2-5.4\" opacity=\"1\" quantity=\"54\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFFF00\" label=\"5.4-5.5\" opacity=\"1\" quantity=\"55\"/\u003e' +\n '\u003cColorMapEntry color=\"#D4FF2B\" label=\"5.5-5.6\" opacity=\"1\" quantity=\"56\"/\u003e' +\n '\u003cColorMapEntry color=\"#AAFF55\" label=\"5.6-5.7\" opacity=\"1\" quantity=\"57\"/\u003e' +\n '\u003cColorMapEntry color=\"#80FF80\" label=\"5.7-5.9\" opacity=\"1\" quantity=\"59\"/\u003e' +\n '\u003cColorMapEntry color=\"#55FFAA\" label=\"5.9-6\" opacity=\"1\" quantity=\"60\"/\u003e' +\n '\u003cColorMapEntry color=\"#2BFFD5\" label=\"6-6.2\" opacity=\"1\" quantity=\"62\"/\u003e' +\n '\u003cColorMapEntry color=\"#00FFFF\" label=\"6.2-6.3\" opacity=\"1\" quantity=\"63\"/\u003e' +\n '\u003cColorMapEntry color=\"#00AAFF\" label=\"6.3-6.6\" opacity=\"1\" quantity=\"66\"/\u003e' +\n '\u003cColorMapEntry color=\"#0055FF\" label=\"6.6-6.8\" opacity=\"1\" quantity=\"68\"/\u003e' +\n '\u003cColorMapEntry color=\"#0000FF\" label=\"6.8-7.1\" opacity=\"1\" quantity=\"71\"/\u003e' +\n '\u003cColorMapEntry color=\"#0000CC\" label=\"7.1-10.5\" opacity=\"1\" quantity=\"76\"/\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=\"#CC0000\" label=\"3.5-4.6\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#FF0000\" label=\"4.6-4.9\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#FF5500\" label=\"4.9-5.2\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFAA00\" label=\"5.2-5.4\" opacity=\"1\" quantity=\"54\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFFF00\" label=\"5.4-5.5\" opacity=\"1\" quantity=\"55\"/\u003e' +\n '\u003cColorMapEntry color=\"#D4FF2B\" label=\"5.5-5.6\" opacity=\"1\" quantity=\"56\"/\u003e' +\n '\u003cColorMapEntry color=\"#AAFF55\" label=\"5.6-5.7\" opacity=\"1\" quantity=\"57\"/\u003e' +\n '\u003cColorMapEntry color=\"#80FF80\" label=\"5.7-5.9\" opacity=\"1\" quantity=\"59\"/\u003e' +\n '\u003cColorMapEntry color=\"#55FFAA\" label=\"5.9-6\" opacity=\"1\" quantity=\"60\"/\u003e' +\n '\u003cColorMapEntry color=\"#2BFFD5\" label=\"6-6.2\" opacity=\"1\" quantity=\"62\"/\u003e' +\n '\u003cColorMapEntry color=\"#00FFFF\" label=\"6.2-6.3\" opacity=\"1\" quantity=\"63\"/\u003e' +\n '\u003cColorMapEntry color=\"#00AAFF\" label=\"6.3-6.6\" opacity=\"1\" quantity=\"66\"/\u003e' +\n '\u003cColorMapEntry color=\"#0055FF\" label=\"6.6-6.8\" opacity=\"1\" quantity=\"68\"/\u003e' +\n '\u003cColorMapEntry color=\"#0000FF\" label=\"6.8-7.1\" opacity=\"1\" quantity=\"71\"/\u003e' +\n '\u003cColorMapEntry color=\"#0000CC\" label=\"7.1-10.5\" opacity=\"1\" quantity=\"76\"/\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';\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/ph\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"ph, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"ph, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"ph, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"ph, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10);\n\nvar visualization = {min: 4, max: 8};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"ph, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_ph) \n[iSDAsoil pH](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_ph) \npH at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with x/10. 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 ... \nISDASOIL/Africa/v1/ph, africa,isda,ph,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_ph)"]]
