iSDAsoil Bulk Density, <2mm Fraction
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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/bulk_density")
open_in_new
Tags
africa
isda
soil
densidade aparente
Descrição
Densidade aparente, fração <2 mm em profundidades de solo de 0 a 20 cm e 20 a 50 cm, média prevista e desvio padrão.
Os valores de pixel precisam ser transformados novamente com x/100.
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_20g/cm³
44
197
metros
Densidade aparente, fração <2 mm, média prevista na profundidade de 0 a 20 cm
mean_20_50g/cm³
44
196
metros
Densidade aparente, fração <2 mm, média prevista a uma profundidade de 20 a 50 cm
stdev_0_20g/cm³
0
92
metros
Densidade aparente, fração <2 mm, desvio padrão na profundidade de 0 a 20 cm
stdev_20_50g/cm³
0
92
metros
Densidade aparente, fração <2 mm, 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="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>' +
'<ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>' +
'<ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>' +
'<ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>' +
'<ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>' +
'<ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>' +
'<ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>' +
'<ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>' +
'<ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>' +
'<ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>' +
'<ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>' +
'<ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>' +
'<ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>' +
'<ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>' +
'<ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#00204D" label="0.8-1.05" opacity="1" quantity="105"/>' +
'<ColorMapEntry color="#002D6C" label="1.05-1.19" opacity="1" quantity="119"/>' +
'<ColorMapEntry color="#16396D" label="1.19-1.23" opacity="1" quantity="123"/>' +
'<ColorMapEntry color="#36476B" label="1.23-1.25" opacity="1" quantity="125"/>' +
'<ColorMapEntry color="#4B546C" label="1.25-1.28" opacity="1" quantity="128"/>' +
'<ColorMapEntry color="#5C616E" label="1.28-1.31" opacity="1" quantity="131"/>' +
'<ColorMapEntry color="#6C6E72" label="1.31-1.34" opacity="1" quantity="134"/>' +
'<ColorMapEntry color="#7C7B78" label="1.34-1.36" opacity="1" quantity="136"/>' +
'<ColorMapEntry color="#8E8A79" label="1.36-1.38" opacity="1" quantity="138"/>' +
'<ColorMapEntry color="#A09877" label="1.38-1.41" opacity="1" quantity="141"/>' +
'<ColorMapEntry color="#B3A772" label="1.41-1.43" opacity="1" quantity="143"/>' +
'<ColorMapEntry color="#C6B66B" label="1.43-1.45" opacity="1" quantity="145"/>' +
'<ColorMapEntry color="#DBC761" label="1.45-1.48" opacity="1" quantity="148"/>' +
'<ColorMapEntry color="#F0D852" label="1.48-1.51" opacity="1" quantity="151"/>' +
'<ColorMapEntry color="#FFEA46" label="1.51-1.85" opacity="1" quantity="154"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_0_20 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var stdev_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' +
'<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="4"/>' +
'<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#440154" label="high" opacity="1" quantity="9"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>' ;
var raw = ee . Image ( "ISDASOIL/Africa/v1/bulk_density" );
Map . addLayer (
raw . select ( 0 ). sldStyle ( mean_0_20 ), {},
"Bulk density, mean visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 1 ). sldStyle ( mean_20_50 ), {},
"Bulk density, mean visualization, 20-50 cm" );
Map . addLayer (
raw . select ( 2 ). sldStyle ( stdev_0_20 ), {},
"Bulk density, stdev visualization, 0-20 cm" );
Map . addLayer (
raw . select ( 3 ). sldStyle ( stdev_20_50 ), {},
"Bulk density, stdev visualization, 20-50 cm" );
var converted = raw . divide ( 100 );
var visualization = { min : 1 , max : 1.5 };
Map . setCenter ( 25 , - 3 , 2 );
Map . addLayer ( converted . select ( 0 ), visualization , "Bulk density, mean, 0-20 cm" );
Abrir no Editor de código
[null,null,[],[[["\u003cp\u003eThis dataset provides soil bulk density data for Africa at 30-meter resolution, covering the period from 2001 to 2017.\u003c/p\u003e\n"],["\u003cp\u003eIt includes predicted mean and standard deviation of bulk density for soil depths of 0-20 cm and 20-50 cm.\u003c/p\u003e\n"],["\u003cp\u003eThe data is derived from machine learning models trained on over 100,000 soil samples and remote sensing data, with potential for lower accuracy in dense jungle areas.\u003c/p\u003e\n"],["\u003cp\u003ePixel values require back-transformation by dividing by 100 to obtain the actual bulk density in g/cm³.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is provided by Innovative Solutions for Decision Agriculture Ltd.(iSDA) under a CC-BY-4.0 license.\u003c/p\u003e\n"]]],[],null,["# iSDAsoil Bulk Density, <2mm Fraction\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) \nbulk-density \n\n#### Description\n\nBulk density, \\\u003c2mm fraction at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation.\n\nPixel values must be back-transformed with `x/100`.\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/cm\\^3 | 44 | 197 | meters | Bulk density, \\\u003c2mm fraction, predicted mean at 0-20 cm depth |\n| `mean_20_50` | g/cm\\^3 | 44 | 196 | meters | Bulk density, \\\u003c2mm fraction, predicted mean at 20-50 cm depth |\n| `stdev_0_20` | g/cm\\^3 | 0 | 92 | meters | Bulk density, \\\u003c2mm fraction, standard deviation at 0-20 cm depth |\n| `stdev_20_50` | g/cm\\^3 | 0 | 92 | meters | Bulk density, \\\u003c2mm fraction, 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=\"0.8-1.05\" opacity=\"1\" quantity=\"105\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"1.05-1.19\" opacity=\"1\" quantity=\"119\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"1.19-1.23\" opacity=\"1\" quantity=\"123\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"1.23-1.25\" opacity=\"1\" quantity=\"125\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"1.25-1.28\" opacity=\"1\" quantity=\"128\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"1.28-1.31\" opacity=\"1\" quantity=\"131\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"1.31-1.34\" opacity=\"1\" quantity=\"134\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"1.34-1.36\" opacity=\"1\" quantity=\"136\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"1.36-1.38\" opacity=\"1\" quantity=\"138\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"1.38-1.41\" opacity=\"1\" quantity=\"141\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"1.41-1.43\" opacity=\"1\" quantity=\"143\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"1.43-1.45\" opacity=\"1\" quantity=\"145\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"1.45-1.48\" opacity=\"1\" quantity=\"148\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"1.48-1.51\" opacity=\"1\" quantity=\"151\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"1.51-1.85\" opacity=\"1\" quantity=\"154\"/\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=\"0.8-1.05\" opacity=\"1\" quantity=\"105\"/\u003e' +\n '\u003cColorMapEntry color=\"#002D6C\" label=\"1.05-1.19\" opacity=\"1\" quantity=\"119\"/\u003e' +\n '\u003cColorMapEntry color=\"#16396D\" label=\"1.19-1.23\" opacity=\"1\" quantity=\"123\"/\u003e' +\n '\u003cColorMapEntry color=\"#36476B\" label=\"1.23-1.25\" opacity=\"1\" quantity=\"125\"/\u003e' +\n '\u003cColorMapEntry color=\"#4B546C\" label=\"1.25-1.28\" opacity=\"1\" quantity=\"128\"/\u003e' +\n '\u003cColorMapEntry color=\"#5C616E\" label=\"1.28-1.31\" opacity=\"1\" quantity=\"131\"/\u003e' +\n '\u003cColorMapEntry color=\"#6C6E72\" label=\"1.31-1.34\" opacity=\"1\" quantity=\"134\"/\u003e' +\n '\u003cColorMapEntry color=\"#7C7B78\" label=\"1.34-1.36\" opacity=\"1\" quantity=\"136\"/\u003e' +\n '\u003cColorMapEntry color=\"#8E8A79\" label=\"1.36-1.38\" opacity=\"1\" quantity=\"138\"/\u003e' +\n '\u003cColorMapEntry color=\"#A09877\" label=\"1.38-1.41\" opacity=\"1\" quantity=\"141\"/\u003e' +\n '\u003cColorMapEntry color=\"#B3A772\" label=\"1.41-1.43\" opacity=\"1\" quantity=\"143\"/\u003e' +\n '\u003cColorMapEntry color=\"#C6B66B\" label=\"1.43-1.45\" opacity=\"1\" quantity=\"145\"/\u003e' +\n '\u003cColorMapEntry color=\"#DBC761\" label=\"1.45-1.48\" opacity=\"1\" quantity=\"148\"/\u003e' +\n '\u003cColorMapEntry color=\"#F0D852\" label=\"1.48-1.51\" opacity=\"1\" quantity=\"151\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFEA46\" label=\"1.51-1.85\" opacity=\"1\" quantity=\"154\"/\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=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"9\"/\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=\"2\"/\u003e' +\n '\u003cColorMapEntry color=\"#5dc962\" label=\" \" opacity=\"1\" quantity=\"4\"/\u003e' +\n '\u003cColorMapEntry color=\"#20908d\" label=\" \" opacity=\"1\" quantity=\"5\"/\u003e' +\n '\u003cColorMapEntry color=\"#3a528b\" label=\" \" opacity=\"1\" quantity=\"7\"/\u003e' +\n '\u003cColorMapEntry color=\"#440154\" label=\"high\" opacity=\"1\" quantity=\"9\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003cContrastEnhancement/\u003e' +\n'\u003c/RasterSymbolizer\u003e';\n\nvar raw = ee.Image(\"ISDASOIL/Africa/v1/bulk_density\");\nMap.addLayer(\n raw.select(0).sldStyle(mean_0_20), {},\n \"Bulk density, mean visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(1).sldStyle(mean_20_50), {},\n \"Bulk density, mean visualization, 20-50 cm\");\nMap.addLayer(\n raw.select(2).sldStyle(stdev_0_20), {},\n \"Bulk density, stdev visualization, 0-20 cm\");\nMap.addLayer(\n raw.select(3).sldStyle(stdev_20_50), {},\n \"Bulk density, stdev visualization, 20-50 cm\");\n\nvar converted = raw.divide(100);\n\nvar visualization = {min: 1, max: 1.5};\n\nMap.setCenter(25, -3, 2);\n\nMap.addLayer(converted.select(0), visualization, \"Bulk density, mean, 0-20 cm\");\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/ISDASOIL/ISDASOIL_Africa_v1_bulk_density) \n[iSDAsoil Bulk Density, \\\u003c2mm Fraction](/earth-engine/datasets/catalog/ISDASOIL_Africa_v1_bulk_density) \nBulk density, \\\u003c2mm fraction at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with x/100. 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 ... \nISDASOIL/Africa/v1/bulk_density, 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_bulk_density)"]]
