iSDAsoil Silt Content
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Dostępność zbioru danych
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Dostawca zbioru danych
iSDA
Fragment kodu Earth Engine
ee.Image("ISDASOIL/Africa/v1/silt_content")
open_in_new
Tagi
africa
isda
soil
Silt
Opis
Zawartość pyłu w glebie na głębokości 0–20 cm i 20–50 cm, przewidywana średnia i odchylenie standardowe.
Wartości pikseli muszą zostać przekształcone z powrotem za pomocą funkcji exp(x/10)-1.
Na obszarach gęstej dżungli (głównie w Afryce Środkowej) dokładność modelu jest niska, dlatego mogą być widoczne artefakty, takie jak pasy.
Prognozy dotyczące właściwości gleby zostały opracowane przez firmę Innovative Solutions for Decision Agriculture Ltd. (iSDA)
w rozdzielczości 30 m na piksel przy użyciu uczenia maszynowego w połączeniu z danymi teledetekcyjnymi
i zbiorem treningowym zawierającym ponad 100 tys. przeanalizowanych próbek gleby.
Więcej informacji znajdziesz w najczęstszych pytaniach i dokumentacji z informacjami technicznymi . Aby zgłosić problem lub poprosić o pomoc, wejdź na stronę iSDAsoil .
Pasma
Rozmiar piksela
Pasma
Nazwa
Jednostki
Minimum
Maks.
Rozmiar piksela
Opis
mean_0_20%
1
61
metry
Zawartość mułu, przewidywana średnia na głębokości 0–20 cm
mean_20_50%
0
62
metry
Zawartość mułu, przewidywana średnia na głębokości 20–50 cm
stdev_0_20%
0
38
metry
Zawartość mułu, odchylenie standardowe na głębokości 0–20 cm
stdev_20_50%
0
38
metry
Zawartość mułu, odchylenie standardowe na głębokości 20–50 cm
Warunki korzystania z usługi
Warunki korzystania z usługi
CC-BY-4.0
Cytaty
Hengl, T., Miller, M.A.E., Križan, J. i in. 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
Odkrywanie za pomocą Earth Engine
Ważne:
Earth Engine to platforma do analizy naukowej i wizualizacji zbiorów danych geoprzestrzennych o rozmiarze petabajtów, która jest przeznaczona zarówno dla użytkowników publicznych, jak i biznesowych oraz rządowych.
Earth Engine jest bezpłatne do użytku w celach badawczych, edukacyjnych i non-profit. Aby rozpocząć, zarejestruj się, aby uzyskać dostęp do Earth Engine .
Edytor kodu (JavaScript)
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" );
Otwórz w edytorze kodu
[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)"]]
