نیتروژن کل در عمق 0-20 سانتی متر و 20-50 سانتی متر، میانگین و انحراف معیار پیش بینی شده است.
مقادیر پیکسل باید با exp(x/100)-1 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
نیتروژن کل در عمق 0-20 سانتی متر و 20-50 سانتی متر، میانگین و انحراف معیار پیش بینی شده است. مقادیر پیکسل باید با exp(x/100)-1 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 مالکیت خاک…
[null,null,[],[[["\u003cp\u003eThe dataset provides spatial predictions of total nitrogen content in African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eIt includes predicted mean and standard deviation values for total nitrogen, derived using machine learning and remote sensing data.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require back-transformation using a specific formula (\u003ccode\u003eexp(x/100)-1\u003c/code\u003e) for accurate representation.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset covers the African continent at a 30-meter resolution and spans from 2001 to 2017.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy might be lower in dense jungle areas, potentially leading to visual artifacts like banding.\u003c/p\u003e\n"]]],["iSDA provides data on total nitrogen in African soils from 2001 to 2017. The dataset includes predicted mean and standard deviation at depths of 0-20 cm and 20-50 cm, with 30-meter pixel size. It requires pixel value back-transformation using `exp(x/100)-1`. Soil property predictions are derived from machine learning, remote sensing data, and over 100,000 soil samples. The model's accuracy is lower in dense jungle areas. The data is accessible via Google Earth Engine.\n"],null,["# iSDAsoil Total Nitrogen\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) \nnitrogen \n\n#### Description\n\nTotal nitrogen 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/100)-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` | g/kg | 3 | 246 | meters | Nitrogen, total, predicted mean at 0-20 cm depth |\n| `mean_20_50` | g/kg | 0 | 254 | meters | Nitrogen, total, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | g/kg | 1 | 124 | meters | Nitrogen, total, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | g/kg | 1 | 125 | meters | Nitrogen, total, 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=\"#000004\" label=\"0-0.2\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"0.2-0.3\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"0.3-0.4\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"0.4-0.5\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"0.5-0.6\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"0.6-0.7\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"0.7-0.8\" opacity=\"1\" quantity=\"58\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"0.8-0.9\" opacity=\"1\" quantity=\"64\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"0.9-1\" opacity=\"1\" quantity=\"67\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"1-1.1\" opacity=\"1\" quantity=\"75\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"1.1-1.2\" opacity=\"1\" quantity=\"79\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"1.2-1.3\" opacity=\"1\" quantity=\"83\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"1.3-1.4\" opacity=\"1\" quantity=\"89\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"1.4-1.5\" opacity=\"1\" quantity=\"93\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"1.5-10\" opacity=\"1\" quantity=\"99\"/\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=\"0-0.2\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"0.2-0.3\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"0.3-0.4\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"0.4-0.5\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"0.5-0.6\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"0.6-0.7\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"0.7-0.8\" opacity=\"1\" quantity=\"58\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"0.8-0.9\" opacity=\"1\" quantity=\"64\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"0.9-1\" opacity=\"1\" quantity=\"67\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"1-1.1\" opacity=\"1\" quantity=\"75\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"1.1-1.2\" opacity=\"1\" quantity=\"79\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"1.2-1.3\" opacity=\"1\" quantity=\"83\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"1.3-1.4\" opacity=\"1\" quantity=\"89\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"1.4-1.5\" opacity=\"1\" quantity=\"93\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"1.5-10\" opacity=\"1\" quantity=\"99\"/\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=\"8\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"60\"/\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=\"8\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"60\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/nitrogen_total\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Nitrogen, total, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Nitrogen, total, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Nitrogen, total, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Nitrogen, total, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(100).exp().subtract(1);\n\nvar visualization = {min: 0, max: 10000};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Nitrogen, total, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_nitrogen_total) \n[iSDAsoil Total Nitrogen](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_nitrogen_total) \nTotal nitrogen 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/100)-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/nitrogen_total, 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_nitrogen_total)"]]
