iSDAsoil Extractable Sulfur
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Disponibilidade de conjuntos de dados
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
Provedor de conjunto de dados
iSDA
Snippet do Earth Engine
ee.Image("ISDASOIL/Africa/v1/sulphur_extractable")
open_in_new
Tags
africa
isda
soil
enxofre
Descrição
Enxofre extraível em profundidades de 0 a 20 cm e 20 a 50 cm, média prevista e desvio padrão.
Os valores de pixel precisam ser transformados novamente com exp(x/10)-1.
Em áreas de selva densa (geralmente na África Central), a acurácia do modelo é baixa e, portanto, podem aparecer artefatos como bandas (listras).
As previsões de propriedades do solo foram feitas pela Innovative Solutions for Decision Agriculture Ltd. (iSDA) com tamanho de pixel de 30 m usando machine learning e dados de sensoriamento remoto,além de um conjunto de treinamento de mais de 100.000 amostras de solo analisadas.
Mais informações podem ser encontradas no
FAQ e na
documentação de informações técnicas . Para enviar um problema ou pedir suporte, acesse o site do iSDAsoil .
Bandas
Tamanho do pixel
Bandas
Nome
Unidades
Mín.
Máx.
Tamanho do pixel
Descrição
mean_0_20ppm
3
42
metros
Enxofre extraível, média prevista na profundidade de 0 a 20 cm
mean_20_50ppm
0
39
metros
Enxofre extraível, média prevista a uma profundidade de 20 a 50 cm
stdev_0_20ppm
1
37
metros
Enxofre extraível, desvio padrão na profundidade de 0 a 20 cm
stdev_20_50ppm
3
41
metros
Enxofre extraível, desvio padrão na profundidade de 20 a 50 cm
Citações
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
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Editor de código (JavaScript)
var mean_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-2.3" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#350498" label="2.3-3.1" opacity="1" quantity="14"/>' +
'<ColorMapEntry color="#5402A3" label="3.1-3.5" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#8B0AA5" label="4-5" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#A31E9A" label="5-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#B93289" label="5.7-6.4" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#CC4678" label="6.4-7.2" opacity="1" quantity="21"/>' +
'<ColorMapEntry color="#DB5C68" label="7.2-8" opacity="1" quantity="22"/>' +
'<ColorMapEntry color="#E97158" label="8-9" opacity="1" quantity="23"/>' +
'<ColorMapEntry color="#F48849" label="9-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#FBA139" label="10-11.2" opacity="1" quantity="25"/>' +
'<ColorMapEntry color="#FEBC2A" label="11.2-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#FADA24" label="12.5-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#F0F921" label="15.4-125" opacity="1" quantity="30"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-2.3" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#350498" label="2.3-3.1" opacity="1" quantity="14"/>' +
'<ColorMapEntry color="#5402A3" label="3.1-3.5" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#8B0AA5" label="4-5" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#A31E9A" label="5-5.7" opacity="1" quantity="19"/>' +
'<ColorMapEntry color="#B93289" label="5.7-6.4" opacity="1" quantity="20"/>' +
'<ColorMapEntry color="#CC4678" label="6.4-7.2" opacity="1" quantity="21"/>' +
'<ColorMapEntry color="#DB5C68" label="7.2-8" opacity="1" quantity="22"/>' +
'<ColorMapEntry color="#E97158" label="8-9" opacity="1" quantity="23"/>' +
'<ColorMapEntry color="#F48849" label="9-10" opacity="1" quantity="24"/>' +
'<ColorMapEntry color="#FBA139" label="10-11.2" opacity="1" quantity="25"/>' +
'<ColorMapEntry color="#FEBC2A" label="11.2-12.5" opacity="1" quantity="26"/>' +
'<ColorMapEntry color="#FADA24" label="12.5-15.4" opacity="1" quantity="28"/>' +
'<ColorMapEntry color="#F0F921" label="15.4-125" opacity="1" quantity="30"/>' +
'</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="6"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' +
'</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="6"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/sulphur_extractable" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Sulphur extractable, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Sulphur extractable, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Sulphur extractable, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Sulphur extractable, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 10 ). exp (). subtract ( 1 );
var visualization = { min : 0 , max : 20 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Sulphur extractable, mean, 0-20 cm" );
Abrir no Editor de código
[null,null,[],[[["\u003cp\u003eThis dataset provides the predicted mean and standard deviation of extractable sulfur at soil depths of 0-20 cm and 20-50 cm for Africa.\u003c/p\u003e\n"],["\u003cp\u003eThe data covers the period from 2001 to 2017 and was produced by Innovative Solutions for Decision Agriculture Ltd.(iSDA).\u003c/p\u003e\n"],["\u003cp\u003ePixel values are stored in a transformed format and require back-transformation using the formula \u003ccode\u003eexp(x/10)-1\u003c/code\u003e to obtain actual sulfur values in ppm.\u003c/p\u003e\n"],["\u003cp\u003eModel accuracy is reduced in areas of dense jungle, particularly in central Africa, potentially leading to visual artifacts.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is available under the CC-BY-4.0 license.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Extractable Sulfur\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) \nsulfur \n\n#### Description\n\nExtractable sulfur 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 | 3 | 42 | meters | Sulfur, extractable, predicted mean at 0-20 cm depth |\n| `mean_20_50` | ppm | 0 | 39 | meters | Sulfur, extractable, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | ppm | 1 | 37 | meters | Sulfur, extractable, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | ppm | 3 | 41 | meters | Sulfur, 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=\"0-2.3\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"2.3-3.1\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"3.1-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"3.5-4\" opacity=\"1\" quantity=\"16\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"4-5\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"5-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"5.7-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"6.4-7.2\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"7.2-8\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"8-9\" opacity=\"1\" quantity=\"23\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"9-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"10-11.2\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"11.2-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"12.5-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"15.4-125\" opacity=\"1\" quantity=\"30\"/\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=\"0-2.3\" opacity=\"1\" quantity=\"12\"/\u003e' +\n '\u003cColorMapEntry color=\"#350498\" label=\"2.3-3.1\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003cColorMapEntry color=\"#5402A3\" label=\"3.1-3.5\" opacity=\"1\" quantity=\"15\"/\u003e' +\n '\u003cColorMapEntry color=\"#7000A8\" label=\"3.5-4\" opacity=\"1\" quantity=\"16\"/\u003e' +\n '\u003cColorMapEntry color=\"#8B0AA5\" label=\"4-5\" opacity=\"1\" quantity=\"18\"/\u003e' +\n '\u003cColorMapEntry color=\"#A31E9A\" label=\"5-5.7\" opacity=\"1\" quantity=\"19\"/\u003e' +\n '\u003cColorMapEntry color=\"#B93289\" label=\"5.7-6.4\" opacity=\"1\" quantity=\"20\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC4678\" label=\"6.4-7.2\" opacity=\"1\" quantity=\"21\"/\u003e' +\n '\u003cColorMapEntry color=\"#DB5C68\" label=\"7.2-8\" opacity=\"1\" quantity=\"22\"/\u003e' +\n '\u003cColorMapEntry color=\"#E97158\" label=\"8-9\" opacity=\"1\" quantity=\"23\"/\u003e' +\n '\u003cColorMapEntry color=\"#F48849\" label=\"9-10\" opacity=\"1\" quantity=\"24\"/\u003e' +\n '\u003cColorMapEntry color=\"#FBA139\" label=\"10-11.2\" opacity=\"1\" quantity=\"25\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEBC2A\" label=\"11.2-12.5\" opacity=\"1\" quantity=\"26\"/\u003e' +\n '\u003cColorMapEntry color=\"#FADA24\" label=\"12.5-15.4\" opacity=\"1\" quantity=\"28\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0F921\" label=\"15.4-125\" opacity=\"1\" quantity=\"30\"/\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=\"6\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"14\"/\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=\"6\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"14\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/sulphur_extractable\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Sulphur extractable, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Sulphur extractable, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Sulphur extractable, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Sulphur extractable, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(10).exp().subtract(1);\n\nvar visualization = {min: 0, max: 20};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Sulphur extractable, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_sulphur_extractable) \n[iSDAsoil Extractable Sulfur](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_sulphur_extractable) \nExtractable sulfur 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/sulphur_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_sulphur_extractable)"]]
