iSDAsoil Silt Content
با مجموعهها، منظم بمانید
ذخیره و طبقهبندی محتوا براساس اولویتهای شما.
در دسترس بودن مجموعه داده 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z ارائه دهنده مجموعه داده iSDA قطعه موتور زمین ee.Image("ISDASOIL/Africa/v1/silt_content") open_in_new برچسب ها لجن خاک آفریقا ایسدا توضیحات محتوای سیلت در عمق 0-20 سانتی متر و 20-50 سانتی متر، میانگین و انحراف معیار پیش بینی شده است.
مقادیر پیکسل باید با exp(x/10)-1 به عقب تبدیل شوند.
در مناطق جنگلی متراکم (به طور کلی بر فراز آفریقای مرکزی)، دقت مدل پایین است و بنابراین ممکن است مصنوعاتی مانند نواربندی (راه راه) دیده شود.
پیش‌بینی ویژگی‌های خاک توسط Innovative Solutions for Decision Agriculture Ltd. (iSDA) در اندازه پیکسل 30 متر با استفاده از یادگیری ماشین همراه با داده‌های سنجش از راه دور و مجموعه آموزشی بیش از 100000 نمونه خاک تجزیه‌شده انجام شد.
اطلاعات بیشتر را می توان در پرسش های متداول و مستندات اطلاعات فنی یافت. برای ارسال مشکل یا درخواست پشتیبانی، لطفاً به سایت iSDAsoil مراجعه کنید.
باندها اندازه پیکسل
باندها
نام واحدها حداقل حداکثر اندازه پیکسل توضیحات mean_0_20 % 1 61 متر محتوای سیلت، میانگین پیش بینی شده در عمق 0-20 سانتی متر
mean_20_50 % 0 62 متر محتوای سیلت، میانگین پیش بینی شده در عمق 20-50 سانتی متری
stdev_0_20 % 0 38 متر محتوای سیلت، انحراف معیار در عمق 0-20 سانتی متر
stdev_20_50 % 0 38 متر محتوای سیلت، انحراف معیار در عمق 20-50 سانتی متر
نقل قول ها Hengl, T., Miller, MAE, Križan, J., et al. ویژگی‌های خاک آفریقا و مواد مغذی با وضوح فضایی 30 متر با استفاده از یادگیری ماشین دو مقیاسی نقشه‌برداری شدند. Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y
با Earth Engine کاوش کنید مهم: Earth Engine یک پلت فرم برای تجزیه و تحلیل علمی در مقیاس پتابایت و تجسم مجموعه داده های مکانی است، هم برای منافع عمومی و هم برای کاربران تجاری و دولتی. Earth Engine برای استفاده تحقیقاتی، آموزشی و غیرانتفاعی رایگان است. برای شروع، لطفاً برای دسترسی به Earth Engine ثبت نام کنید. ویرایشگر کد (جاوا اسکریپت)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#00204D" label="0-7" opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>' +
'<ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>' +
'<ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>' +
'<ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>' +
'<ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>' +
'<ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>' +
'<ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#00204D" label="0-7" opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>' +
'<ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>' +
'<ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>' +
'<ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>' +
'<ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>' +
'<ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>' +
'<ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>' +
'</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="4.19000000000005"/>' +
'</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="4.19000000000005"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/silt_content" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Silt content, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Silt content, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Silt content, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Silt content, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 10 ). exp (). subtract ( 1 );
var visualization = { min : 0 , max : 15 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Silt content, mean, 0-20 cm" ); در ویرایشگر کد باز کنید
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of silt content 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 reduced 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 consult the provided FAQ and technical documentation for further information.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Silt Content\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) \nsilt \n\n#### Description\n\nSilt content 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` | % | 1 | 61 | meters | Silt content, predicted mean at 0-20 cm depth |\n| `mean_20_50` | % | 0 | 62 | meters | Silt content, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | % | 0 | 38 | meters | Silt content, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | % | 0 | 38 | meters | Silt content, 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=\"#00204D\" label=\"0-7\" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"7-9\" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"9-10\" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"10-11\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"11-12\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"12-13\" opacity=\"1\" quantity=\"13\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"13-14\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"14-15\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"15-16\" opacity=\"1\" quantity=\"16\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"16-17\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"17-18\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"18-19\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"19-20\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"20-22\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"22-70\" opacity=\"1\" quantity=\"24\"/\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=\"#00204D\" label=\"0-7\" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"7-9\" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"9-10\" opacity=\"1\" quantity=\"10\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"10-11\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"11-12\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"12-13\" opacity=\"1\" quantity=\"13\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"13-14\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"14-15\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"15-16\" opacity=\"1\" quantity=\"16\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"16-17\" opacity=\"1\" quantity=\"17\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"17-18\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"18-19\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"19-20\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"20-22\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"22-70\" opacity=\"1\" quantity=\"24\"/\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=\"4.19000000000005\"/\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=\"4.19000000000005\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/silt_content\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Silt content, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Silt content, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Silt content, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Silt content, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 15};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Silt content, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_silt_content) \n[iSDAsoil Silt Content](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_silt_content) \nSilt content 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/silt_content, 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_silt_content)"]]
