0-20 সেমি এবং 20-50 সেমি মাটির গভীরতায় বালির পরিমাণ,\nঅনুমানিত গড় এবং মানক বিচ্যুতি। ঘন জঙ্গলের এলাকায় (সাধারণত মধ্য আফ্রিকার উপরে), মডেলের নির্ভুলতা কম এবং তাই ব্যান্ডিং (স্ট্রাইপিং) এর মতো শিল্পকর্ম দেখা যেতে পারে।
ইনোভেটিভ সলিউশন ফর ডিসিশন এগ্রিকালচার লিমিটেড (iSDA) দ্বারা 30 মিটার পিক্সেল আকারে মেশিন লার্নিং এবং রিমোট সেন্সিং ডেটা এবং 100,000 টিরও বেশি বিশ্লেষণ করা মাটির নমুনার একটি প্রশিক্ষণ সেট ব্যবহার করে মাটির সম্পত্তির ভবিষ্যদ্বাণী করা হয়েছিল।
Hengl, T., Miller, MAE, Križan, J., et al. আফ্রিকান মাটির বৈশিষ্ট্য এবং পুষ্টি উপাদান 30 মিটার স্থানিক রেজোলিউশনে ম্যাপ করা হয়েছে টু-স্কেল এনসেম্বল মেশিন লার্নিং ব্যবহার করে। Sci Rep 11, 6130 (2021)। doi:10.1038/s41598-021-85639-y
0-20 সেমি এবং 20-50 সেমি মাটির গভীরতায় বালির পরিমাণ,\nঅনুমানিত গড় এবং মানক বিচ্যুতি। ঘন জঙ্গলের এলাকায় (সাধারণত মধ্য আফ্রিকার উপরে), মডেলের নির্ভুলতা কম এবং তাই ব্যান্ডিং (স্ট্রাইপিং) এর মতো শিল্পকর্ম দেখা যেতে পারে। সিদ্ধান্ত কৃষি লিমিটেডের জন্য উদ্ভাবনী সমাধান দ্বারা মৃত্তিকা সম্পত্তি ভবিষ্যদ্বাণী করা হয়েছিল। …
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of sand content in African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eThe data covers the period from 2001 to 2017 and was produced by iSDA using machine learning and remote sensing data.\u003c/p\u003e\n"],["\u003cp\u003eThe spatial resolution of the dataset is 30 meters and it's available for Africa.\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"],["\u003cp\u003eThe data is licensed under CC-BY-4.0 and can be accessed and analyzed using Google Earth Engine.\u003c/p\u003e\n"]]],["The dataset, provided by iSDA, offers predicted sand content in African soil from 2001 to 2017. Data is available at depths of 0-20 cm and 20-50 cm, including predicted mean and standard deviation. Utilizing machine learning and remote sensing, iSDA analyzed over 100,000 soil samples to create the dataset with a 30-meter pixel size. This is accessible through Google Earth Engine, identified by the `ee.Image(\"ISDASOIL/Africa/v1/sand_content\")` snippet.\n"],null,["# iSDAsoil Sand 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) [sand](/earth-engine/datasets/tags/sand) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\nSand 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` | % | 2 | 94 | meters | Sand content, predicted mean at 0-20 cm depth |\n| `mean_20_50` | % | 2 | 95 | meters | Sand content, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | % | 0 | 144 | meters | Sand content, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | % | 0 | 143 | meters | Sand 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=\"0-31\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"31-39\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"39-43\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"43-46\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"46-49\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"49-52\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"52-54\" opacity=\"1\" quantity=\"54\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"54-56\" opacity=\"1\" quantity=\"56\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"56-58\" opacity=\"1\" quantity=\"58\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"58-60\" opacity=\"1\" quantity=\"60\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"60-63\" opacity=\"1\" quantity=\"63\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"63-65\" opacity=\"1\" quantity=\"65\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"65-68\" opacity=\"1\" quantity=\"68\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"68-71\" opacity=\"1\" quantity=\"71\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"71-100\" opacity=\"1\" quantity=\"75\"/\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=\"0-31\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"31-39\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"39-43\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"43-46\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"46-49\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"49-52\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"52-54\" opacity=\"1\" quantity=\"54\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"54-56\" opacity=\"1\" quantity=\"56\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"56-58\" opacity=\"1\" quantity=\"58\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"58-60\" opacity=\"1\" quantity=\"60\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"60-63\" opacity=\"1\" quantity=\"63\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"63-65\" opacity=\"1\" quantity=\"65\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"65-68\" opacity=\"1\" quantity=\"68\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"68-71\" opacity=\"1\" quantity=\"71\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"71-100\" opacity=\"1\" quantity=\"75\"/\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=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"6\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"7\"/\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=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"6\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/sand_content\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Sand content, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Sand content, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Sand content, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Sand content, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 3000};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Sand content, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_sand_content) \n[iSDAsoil Sand Content](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_sand_content) \nSand 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/sand_content, africa,isda,sand,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_sand_content)"]]
