iSDAsoil Extractable Iron
קל לארגן דפים בעזרת אוספים
אפשר לשמור ולסווג תוכן על סמך ההעדפות שלך.
זמינות מערך הנתונים
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
ספק מערך נתונים
iSDA
Earth Engine Snippet
ee.Image("ISDASOIL/Africa/v1/iron_extractable")
open_in_new
תגים
africa
isda
soil
ברזל
תיאור
ברזל שניתן למיצוי בעומקי קרקע של 0-20 ס"מ ו-20-50 ס"מ,
ממוצע צפוי וסטיית תקן.
צריך לבצע טרנספורמציה הפוכה של ערכי הפיקסלים באמצעות exp(x/10)-1.
באזורים של ג'ונגל צפוף (בדרך כלל מעל מרכז אפריקה), רמת הדיוק של המודל נמוכה ולכן יכול להיות שיוצגו ארטיפקטים כמו פסים.
החיזויים של תכונות הקרקע בוצעו על ידי Innovative Solutions for Decision Agriculture Ltd. (iSDA) בגודל פיקסל של 30 מ', באמצעות למידת מכונה בשילוב עם נתונים של חישה מרחוק ומערך אימון של יותר מ-100,000 דגימות קרקע מנותחות.
מידע נוסף זמין בשאלות הנפוצות ובמסמכי המידע הטכני . כדי לשלוח בעיה או לבקש תמיכה, אפשר להיכנס אל האתר של iSDAsoil .
תחום תדרים
גודל הפיקסל
תחום תדרים
שם
יחידות
מינימום
מקסימום
גודל הפיקסל
תיאור
mean_0_20ppm
0
62
מטרים
ברזל, ניתן למיצוי, ממוצע צפוי בעומק של 0-20 ס"מ
mean_20_50ppm
0
47
מטרים
ברזל, ניתן למיצוי, ממוצע צפוי בעומק של 20-50 ס"מ
stdev_0_20ppm
0
39
מטרים
ברזל, ניתן למיצוי, סטיית תקן בעומק של 0-20 ס"מ
stdev_20_50ppm
0
39
מטרים
ברזל, ניתן למיצוי, סטיית תקן בעומק של 20-50 ס"מ
ציטוטים ביבליוגרפיים
Hengl, T., Miller, M.A.E., קריז'אן, ג'., ואחרים. מיפוי של תכונות ורכיבי תזונה בקרקע באפריקה ברזולוציה מרחבית של 30 מ' באמצעות למידת מכונה בהרכב דו-קנה מידה.
Sci Rep 11, 6130 (2021).
doi:10.1038/s41598-021-85639-y
סיור באמצעות Earth Engine
חשוב:
Earth Engine היא פלטפורמה לניתוח מדעי של נתונים גיאוספציאליים ולהצגתם באופן חזותי, בקנה מידה של פטה-בייט. הפלטפורמה מיועדת לשימוש הציבור, לעסקים ולגופים ממשלתיים.
השימוש ב-Earth Engine הוא בחינם למטרות מחקר, חינוך ולשימוש של עמותות. כדי להתחיל, צריך להירשם לגישה ל-Earth Engine .
עורך הקוד (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" );
פתיחה בעורך הקוד
[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)"]]
