0-20 সেমি এবং 20-50 সেমি মাটির গভীরতায় কার্যকর ক্যাটেশন এক্সচেঞ্জ ক্ষমতা ভবিষ্যদ্বাণী করা গড় এবং আদর্শ বিচ্যুতি,
পিক্সেল মান অবশ্যই exp(x/10)-1 এর সাথে ব্যাক-ট্রান্সফর্ম করতে হবে।
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
কার্যকর Cation বিনিময় ক্ষমতা 0-20 সেমি এবং 20-50 সেমি মাটির গভীরতায় গড় এবং মানক বিচ্যুতির পূর্বাভাস, পিক্সেল মান অবশ্যই exp(x/10)-1 এর সাথে ব্যাক-ট্রান্সফর্ম করতে হবে। ঘন জঙ্গলের এলাকায় (সাধারণত মধ্য আফ্রিকার উপরে), মডেলের নির্ভুলতা কম এবং তাই ব্যান্ডিং (স্ট্রাইপিং) এর মতো শিল্পকর্ম দেখা যেতে পারে। মাটির সম্পত্তি…
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of Effective Cation Exchange Capacity (ECEC) for African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eThe data was produced by iSDA using machine learning models trained on over 100,000 soil samples and remote sensing data, resulting in a 30-meter resolution dataset.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require back-transformation using the formula \u003ccode\u003eexp(x/10)-1\u003c/code\u003e to obtain the ECEC values in cmol(+)/kg.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset covers the period from 2001 to 2017 and is available under the CC-BY-4.0 license.\u003c/p\u003e\n"],["\u003cp\u003eUsers should be aware that model accuracy is lower in dense jungle areas, potentially leading to visual artifacts.\u003c/p\u003e\n"]]],["The iSDA provides a dataset of soil Cation Exchange Capacity for Africa from 2001 to 2017. It includes predicted mean and standard deviation at 0-20 cm and 20-50 cm depths, with 30-meter pixel resolution. Pixel values require back-transformation using `exp(x/10)-1`. Data is accessible through Earth Engine, using the code `ee.Image(\"ISDASOIL/Africa/v1/cation_exchange_capacity\")`. Model accuracy may be lower in dense jungle areas. The data is licensed under CC-BY-4.0.\n"],null,["# iSDAsoil Effective Cation Exchange Capacity\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) [aluminium](/earth-engine/datasets/tags/aluminium) [isda](/earth-engine/datasets/tags/isda) [soil](/earth-engine/datasets/tags/soil) \n\n#### Description\n\nEffective Cation Exchange Capacity predicted mean and standard deviation\nat soil depths of 0-20 cm and 20-50 cm,\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` | cmol(+)/kg | 0 | 45 | meters | Effective Cation Exchange Capacity, predicted mean at 0-20 cm depth |\n| `mean_20_50` | cmol(+)/kg | 0 | 46 | meters | Effective Cation Exchange Capacity, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | cmol(+)/kg | 0 | 19 | meters | Effective Cation Exchange Capacity, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | cmol(+)/kg | 0 | 20 | meters | Effective Cation Exchange Capacity, 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=\"0-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"3.5-4.5\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"4.5-5\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"5-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"6.4-7.2\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"7.2-8\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"8-9\" opacity=\"1\" quantity=\"23\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"9-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"10-11.2\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"11.2-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"19.1-130\" opacity=\"1\" quantity=\"31\"/\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=\"0-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"3.5-4.5\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"4.5-5\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"5-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"6.4-7.2\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"7.2-8\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"8-9\" opacity=\"1\" quantity=\"23\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"9-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"10-11.2\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"11.2-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"19.1-130\" opacity=\"1\" quantity=\"31\"/\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/cation_exchange_capacity\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Cation exchange capacity, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Cation exchange capacity, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Cation exchange capacity, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Cation exchange capacity, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 25};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Cation exchange capacity, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_cation_exchange_capacity) \n[iSDAsoil Effective Cation Exchange Capacity](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_cation_exchange_capacity) \nEffective Cation Exchange Capacity predicted mean and standard deviation at soil depths of 0-20 cm and 20-50 cm, 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 ... \nISDASOIL/Africa/v1/cation_exchange_capacity, africa,aluminium,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_cation_exchange_capacity)"]]
