मिट्टी की 0-20 सेमी और 20-50 सेमी की गहराई पर, अनुमानित माध्य और मानक विचलन के साथ-साथ, प्रभावी कैटायन एक्सचेंज क्षमता,
पिक्सल वैल्यू को exp(x/10)-1 के साथ वापस बदलना होगा.
घने जंगल वाले इलाकों (आम तौर पर मध्य अफ़्रीका) में, मॉडल की सटीकता कम होती है. इसलिए, बैंडिंग (स्ट्राइपिंग) जैसे आर्टफ़ैक्ट दिख सकते हैं.
मिट्टी की प्रॉपर्टी के बारे में अनुमान, Innovative Solutions for Decision Agriculture Ltd. (iSDA) ने लगाए थे. इसके लिए, 30 मीटर पिक्सल साइज़ का इस्तेमाल किया गया था. साथ ही, रिमोट सेंसिंग डेटा के साथ मशीन लर्निंग का इस्तेमाल किया गया था. इसके अलावा, मिट्टी के 1,00,000 से ज़्यादा विश्लेषण किए गए सैंपल का ट्रेनिंग सेट भी इस्तेमाल किया गया था.
टॉम हेंगल, मिलर, एम॰ए॰ई॰, क्रिशनन, जे., वगैरह. अफ़्रीका की मिट्टी की प्रॉपर्टी और पोषक तत्वों को 30 मीटर के स्पेशल रिज़ॉल्यूशन पर मैप किया गया है. इसके लिए, दो-स्केल वाले एनसेंबल मशीन लर्निंग का इस्तेमाल किया गया है.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
मिट्टी की गहराई 0-20 सेमी और 20-50 सेमी पर, कैटायन एक्सचेंज की क्षमता का अनुमानित औसत और मानक विचलन. पिक्सल वैल्यू को exp(x/10)-1 से वापस बदलना होगा. घने जंगल वाले इलाकों (आम तौर पर, मध्य अफ़्रीका) में, मॉडल की सटीकता कम होती है. इसलिए, बैंडिंग (स्ट्राइपिंग) जैसे आर्टफ़ैक्ट दिख सकते हैं. मिट्टी की प्रॉपर्टी …
[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)"]]
