iSDAsoil Extractable Iron
تنظيم صفحاتك في مجموعات
يمكنك حفظ المحتوى وتصنيفه حسب إعداداتك المفضّلة.
توفّر مجموعة البيانات
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
مزوّد مجموعة البيانات
iSDA
مقتطف Earth Engine
ee.Image("ISDASOIL/Africa/v1/iron_extractable")
open_in_new
العلامات
أفريقيا
ISDA
التربة
حديد
الوصف
الحديد القابل للاستخراج على أعماق التربة من 0 إلى 20 سم ومن 20 إلى 50 سم،
المتوسط المتوقّع والانحراف المعياري
يجب إعادة تحويل قيم البكسل باستخدام exp(x/10)-1.
في مناطق الغابات الكثيفة (بشكل عام في وسط أفريقيا)، تكون دقة النموذج منخفضة، وبالتالي قد تظهر تشوّهات، مثل التخطيط (التشريط).
تم إعداد توقّعات حول خصائص التربة بواسطة
Innovative Solutions for Decision Agriculture Ltd. (iSDA)
بدقة 30 مترًا لكل بكسل باستخدام تعلُّم الآلة مع بيانات الاستشعار عن بُعد
ومجموعة تدريب تضم أكثر من 100,000 عيّنة تربة تم تحليلها.
يمكنك الاطّلاع على مزيد من المعلومات في الأسئلة الشائعة والمستندات التي تتضمّن معلومات فنية . لإرسال مشكلة أو طلب الحصول على الدعم، يُرجى الانتقال إلى
موقع iSDAsoil الإلكتروني .
النطاقات
حجم البكسل
النطاقات
الاسم
الوحدات
الحد الأدنى
الحد الأقصى
حجم البكسل
الوصف
mean_0_20ppm
0
62
أمتار
الحديد القابل للاستخراج، المتوسط المتوقّع على عمق 0-20 سم
mean_20_50ppm
0
47
أمتار
الحديد، القابل للاستخراج، المتوسط المتوقّع على عمق 20-50 سم
stdev_0_20ppm
0
39
أمتار
الحديد القابل للاستخراج، الانحراف المعياري على عمق 0-20 سم
stdev_20_50ppm
0
39
أمتار
الحديد القابل للاستخراج، الانحراف المعياري على عمق 20-50 سم
الاقتباسات
Hengl, T., Miller, M.A.E., Križan, J., et al. African soil properties and nutrients
mapped at 30 m spatial resolution using two-scale ensemble machine learning.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
الاستكشاف باستخدام Earth Engine
ملاحظة مهمة:
Earth Engine هي منصة لتحليل البيانات الجغرافية المكانية وعرضها بصريًا على نطاق واسع (بايتابايت)،
وذلك لتحقيق المنفعة العامة ولخدمة المستخدمين من المؤسسات والجهات الحكومية.
يمكن استخدام Earth Engine مجانًا لأغراض البحث والتعليم والاستخدام غير الربحي. للبدء، يُرجى
التسجيل للحصول على إذن الوصول إلى Earth Engine.
أداة تعديل الرموز (JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-6.4" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#350498" label="6.4-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#5402A3" label="13.9-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#7000A8" label="29-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#8B0AA5" label="35.6-43.7" opacity="1" quantity="38"/>' +
'<ColorMapEntry color="#A31E9A" label="43.7-48.4" opacity="1" quantity="39"/>' +
'<ColorMapEntry color="#B93289" label="48.4-53.6" opacity="1" quantity="40"/>' +
'<ColorMapEntry color="#CC4678" label="53.6-59.3" opacity="1" quantity="41"/>' +
'<ColorMapEntry color="#DB5C68" label="59.3-65.7" opacity="1" quantity="42"/>' +
'<ColorMapEntry color="#E97158" label="65.7-72.7" opacity="1" quantity="43"/>' +
'<ColorMapEntry color="#F48849" label="72.7-80.5" opacity="1" quantity="44"/>' +
'<ColorMapEntry color="#FBA139" label="80.5-89" opacity="1" quantity="45"/>' +
'<ColorMapEntry color="#FEBC2A" label="89-98.5" opacity="1" quantity="46"/>' +
'<ColorMapEntry color="#FADA24" label="98.5-108.9" opacity="1" quantity="47"/>' +
'<ColorMapEntry color="#F0F921" label="108.9-1200" opacity="1" quantity="48"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-6.4" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#350498" label="6.4-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#5402A3" label="13.9-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#7000A8" label="29-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#8B0AA5" label="35.6-43.7" opacity="1" quantity="38"/>' +
'<ColorMapEntry color="#A31E9A" label="43.7-48.4" opacity="1" quantity="39"/>' +
'<ColorMapEntry color="#B93289" label="48.4-53.6" opacity="1" quantity="40"/>' +
'<ColorMapEntry color="#CC4678" label="53.6-59.3" opacity="1" quantity="41"/>' +
'<ColorMapEntry color="#DB5C68" label="59.3-65.7" opacity="1" quantity="42"/>' +
'<ColorMapEntry color="#E97158" label="65.7-72.7" opacity="1" quantity="43"/>' +
'<ColorMapEntry color="#F48849" label="72.7-80.5" opacity="1" quantity="44"/>' +
'<ColorMapEntry color="#FBA139" label="80.5-89" opacity="1" quantity="45"/>' +
'<ColorMapEntry color="#FEBC2A" label="89-98.5" opacity="1" quantity="46"/>' +
'<ColorMapEntry color="#FADA24" label="98.5-108.9" opacity="1" quantity="47"/>' +
'<ColorMapEntry color="#F0F921" label="108.9-1200" opacity="1" quantity="48"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="1"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="2"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/iron_extractable" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Iron, extractable, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Iron, extractable, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Iron, extractable, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Iron, extractable, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 10 ). exp (). subtract ( 1 );
var visualization = { min : 0 , max : 140 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Iron, extractable, mean, 0-20 cm" );
فتح في "أداة تعديل الرموز"
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of extractable iron 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\u003ePixel values require back-transformation using the formula \u003ccode\u003eexp(x/10)-1\u003c/code\u003e for analysis.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is lower in dense jungle areas, potentially leading to visual artifacts.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available under the CC-BY-4.0 license and users are encouraged to cite the relevant scientific publication.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Extractable Iron\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) \niron \n\n#### Description\n\nExtractable iron 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 | 0 | 62 | meters | Iron, extractable, predicted mean at 0-20 cm depth |\n| `mean_20_50` | ppm | 0 | 47 | meters | Iron, extractable, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | ppm | 0 | 39 | meters | Iron, extractable, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | ppm | 0 | 39 | meters | Iron, 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=\"0-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"6.4-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"13.9-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"29-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"35.6-43.7\" opacity=\"1\" quantity=\"38\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"43.7-48.4\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"48.4-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"53.6-59.3\" opacity=\"1\" quantity=\"41\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"59.3-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"65.7-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"72.7-80.5\" opacity=\"1\" quantity=\"44\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"80.5-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"89-98.5\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"98.5-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"108.9-1200\" opacity=\"1\" quantity=\"48\"/\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=\"0-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"6.4-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"13.9-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"29-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"35.6-43.7\" opacity=\"1\" quantity=\"38\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"43.7-48.4\" opacity=\"1\" quantity=\"39\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"48.4-53.6\" opacity=\"1\" quantity=\"40\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"53.6-59.3\" opacity=\"1\" quantity=\"41\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"59.3-65.7\" opacity=\"1\" quantity=\"42\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"65.7-72.7\" opacity=\"1\" quantity=\"43\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"72.7-80.5\" opacity=\"1\" quantity=\"44\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"80.5-89\" opacity=\"1\" quantity=\"45\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"89-98.5\" opacity=\"1\" quantity=\"46\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"98.5-108.9\" opacity=\"1\" quantity=\"47\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"108.9-1200\" opacity=\"1\" quantity=\"48\"/\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=\"6\"/\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=\"6\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/iron_extractable\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Iron, extractable, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Iron, extractable, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Iron, extractable, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Iron, extractable, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 140};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Iron, extractable, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_iron_extractable) \n[iSDAsoil Extractable Iron](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_iron_extractable) \nExtractable iron 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/iron_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_iron_extractable)"]]
