iSDAsoil Total Carbon
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Описание Общее содержание углерода на глубине почвы 0-20 см и 20-50 см, прогнозируемое среднее значение и стандартное отклонение.
Значения пикселей должны быть обратно преобразованы с помощью exp(x/10)-1 .
В районах густых джунглей (как правило, в Центральной Африке) точность модели низкая, поэтому могут быть видны такие артефакты, как полосатость.
Прогнозы свойств почвы были сделаны компанией Innovative Solutions for Decision Agriculture Ltd. (iSDA) с размером пикселя 30 м с использованием машинного обучения в сочетании с данными дистанционного зондирования и обучающим набором из более чем 100 000 проанализированных образцов почвы.
Дополнительную информацию можно найти в разделе часто задаваемых вопросов и технической документации . Чтобы сообщить о проблеме или запросить поддержку, посетите сайт iSDAsoil .
Группы Размер пикселя
Группы
Имя Единицы Мин. Макс Размер пикселя Описание mean_0_20 г/кг 0 58 метров Углерод, общий, прогнозируемое среднее на глубине 0–20 см
mean_20_50 г/кг 0 55 метров Углерод, общий, прогнозируемое среднее на глубине 20–50 см
stdev_0_20 г/кг 0 151 метров Углерод общий, стандартное отклонение на глубине 0–20 см
stdev_20_50 г/кг 0 150 метров Углерод общий, стандартное отклонение на глубине 20–50 см
Условия эксплуатации Условия эксплуатации
CC-BY-4.0
Цитаты Хенгль, Т., Миллер, М.А.Э., Крижан, Дж. и др. Свойства и питательные вещества африканских почв, картированные с пространственным разрешением 30 м с использованием двухмасштабного ансамблевого машинного обучения. Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y
Исследуйте с Earth Engine Важно: Earth Engine — это платформа для научного анализа и визуализации геопространственных данных петабайтного масштаба, предназначенная как для общественного пользования, так и для коммерческих и государственных организаций. Earth Engine можно использовать бесплатно в исследовательских, образовательных и некоммерческих целях. Чтобы начать работу, зарегистрируйтесь для получения доступа к Earth Engine. Редактор кода (JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' +
'<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' +
'<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' +
'<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' +
'<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' +
'<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' +
'<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' +
'<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' +
'<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' +
'<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' +
'<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' +
'<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' +
'<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' +
'<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' +
'<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' +
'</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="3"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>' +
'<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="3"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/carbon_total" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Carbon, total, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Carbon, total, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Carbon, total, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Carbon, total, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 10 ). exp (). subtract ( 1 );
var visualization = { min : 0 , max : 60 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Carbon, total, mean, 0-20 cm" ); Открыть в редакторе кода
[null,null,[],[[["\u003cp\u003eThe ISDASOIL/Africa/v1/carbon_total dataset provides predictions for total carbon content in African soil at two depths (0-20 cm and 20-50 cm).\u003c/p\u003e\n"],["\u003cp\u003eData includes predicted mean and standard deviation of total carbon, requiring back-transformation using \u003ccode\u003eexp(x/10)-1\u003c/code\u003e for actual values.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset covers the period from 2001 to 2017 and has a 30-meter resolution.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is potentially lower in dense jungle areas, leading to possible artifacts like banding.\u003c/p\u003e\n"],["\u003cp\u003eiSDA developed the dataset using machine learning and remote sensing data, coupled with extensive soil sample analysis.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Total Carbon\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\nTotal carbon 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` | g/kg | 0 | 58 | meters | Carbon, total, predicted mean at 0-20 cm depth |\n| `mean_20_50` | g/kg | 0 | 55 | meters | Carbon, total, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | g/kg | 0 | 151 | meters | Carbon, total, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | g/kg | 0 | 150 | meters | Carbon, total, 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=\"0-2\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"5.7-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"10-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"19.1-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"21.2-23.5\" opacity=\"1\" quantity=\"32\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"23.5-26.1\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"26.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"29-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"32.1-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"35.6-40\" opacity=\"1\" quantity=\"39\"/\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=\"0-2\" opacity=\"1\" quantity=\"11\"/\u003e' +\n '\u003cColorMapEntry color=\"#0C0927\" label=\"2-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#231151\" label=\"5.7-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#410F75\" label=\"10-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#5F187F\" label=\"12.5-13.9\" opacity=\"1\" quantity=\"27\"/\u003e' +\n '\u003cColorMapEntry color=\"#7B2382\" label=\"13.9-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#982D80\" label=\"15.4-17.2\" opacity=\"1\" quantity=\"29\"/\u003e' +\n '\u003cColorMapEntry color=\"#B63679\" label=\"17.2-19.1\" opacity=\"1\" quantity=\"30\"/\u003e' +\n '\u003cColorMapEntry color=\"#D3436E\" label=\"19.1-21.2\" opacity=\"1\" quantity=\"31\"/\u003e' +\n '\u003cColorMapEntry color=\"#EB5760\" label=\"21.2-23.5\" opacity=\"1\" quantity=\"32\"/\u003e' +\n '\u003cColorMapEntry color=\"#F8765C\" label=\"23.5-26.1\" opacity=\"1\" quantity=\"33\"/\u003e' +\n '\u003cColorMapEntry color=\"#FD9969\" label=\"26.1-29\" opacity=\"1\" quantity=\"34\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBA80\" label=\"29-32.1\" opacity=\"1\" quantity=\"35\"/\u003e' +\n '\u003cColorMapEntry color=\"#FDDC9E\" label=\"32.1-35.6\" opacity=\"1\" quantity=\"36\"/\u003e' +\n '\u003cColorMapEntry color=\"#FCFDBF\" label=\"35.6-40\" opacity=\"1\" quantity=\"39\"/\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=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"5\"/\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=\"3\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"5\"/\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/carbon_total\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Carbon, total, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Carbon, total, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Carbon, total, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Carbon, total, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 60};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Carbon, total, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_carbon_total) \n[iSDAsoil Total Carbon](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_carbon_total) \nTotal carbon 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/carbon_total, 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_carbon_total)"]]
