मिट्टी की 0-20 सेमी और 20-50 सेमी की गहराई पर, मैग्नीशियम की मात्रा का अनुमानित औसत और मानक विचलन.
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 0-20 सेमी और 20-50 सेमी की गहराई पर, मैग्नीशियम की अनुमानित मात्रा, अनुमानित औसत, और मानक विचलन. पिक्सेल वैल्यू को exp(x/10)-1 फ़ॉर्मूले का इस्तेमाल करके वापस बदला जाना चाहिए. 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 …
[null,null,[],[[["\u003cp\u003eThis dataset provides predictions for extractable magnesium in African soil at two depths (0-20 cm and 20-50 cm), including both mean and standard deviation values.\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 with a 30-meter resolution.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require a back-transformation using the formula \u003ccode\u003eexp(x/10)-1\u003c/code\u003e to obtain actual magnesium concentrations in ppm.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is lower in dense jungle areas, potentially leading to visual artifacts like banding.\u003c/p\u003e\n"],["\u003cp\u003eThis dataset is licensed under CC-BY-4.0 and users should refer to the provided citation and iSDAsoil resources for further information.\u003c/p\u003e\n"]]],["The dataset provides extractable magnesium levels in African soils from 2001 to 2017, measured at 0-20 cm and 20-50 cm depths. Data includes predicted mean and standard deviation, with 30-meter pixel resolution. Pixel values require transformation using `exp(x/10)-1`. iSDA generated the dataset through machine learning and over 100,000 soil samples. Accuracy can be lower in dense jungle regions. Users can access and analyze the data through Google Earth Engine.\n"],null,["# iSDAsoil Extractable Magnesium\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) \nmagnesium \n\n#### Description\n\nExtractable magnesium 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/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` | ppm | 16 | 80 | meters | Magnesium, extractable, predicted mean at 0-20 cm depth |\n| `mean_20_50` | ppm | 12 | 80 | meters | Magnesium, extractable, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | ppm | 0 | 76 | meters | Magnesium, extractable, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | ppm | 0 | 76 | meters | Magnesium, extractable, 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=\"#0D0887\" label=\"0-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"19.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"29-39.4\" opacity=\"1\" quantity=\"37\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"39.4-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"53.6-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"72.7-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"89-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"108.9-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"120.5-133.3\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"133.3-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"163-180.3\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"180.3-220.4\" opacity=\"1\" quantity=\"54\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"220.4-243.7\" opacity=\"1\" quantity=\"55\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"243.7-297.9\" opacity=\"1\" quantity=\"57\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"243.7-1200\" opacity=\"1\" quantity=\"60\"/\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=\"#0D0887\" label=\"0-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"19.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"29-39.4\" opacity=\"1\" quantity=\"37\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"39.4-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"53.6-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"72.7-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"89-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"108.9-120.5\" opacity=\"1\" quantity=\"48\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"120.5-133.3\" opacity=\"1\" quantity=\"49\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"133.3-163\" opacity=\"1\" quantity=\"51\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"163-180.3\" opacity=\"1\" quantity=\"52\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"180.3-220.4\" opacity=\"1\" quantity=\"54\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"220.4-243.7\" opacity=\"1\" quantity=\"55\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"243.7-297.9\" opacity=\"1\" quantity=\"57\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"243.7-1200\" opacity=\"1\" quantity=\"60\"/\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/magnesium_extractable\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Magnesium, extractable, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Magnesium, extractable, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Magnesium, extractable, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Magnesium, extractable, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 500};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Magnesium, extractable, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_magnesium_extractable) \n[iSDAsoil Extractable Magnesium](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_magnesium_extractable) \nExtractable magnesium 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/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 predictions were ... \nISDASOIL/Africa/v1/magnesium_extractable, 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_magnesium_extractable)"]]
