iSDAsoil extractable Aluminium
Extractable aluminium 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.
Soil property predictions were made by Innovative Solutions for Decision Agriculture Ltd. (iSDA) at 30 m pixel size using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples.
Further information can be found in the FAQ and technical information documentation. To submit an issue or request support, please visit the iSDAsoil site.
In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artefacts such as banding (striping) might be seen.
Soil property predictions were made by Innovative Solutions for Decision Agriculture Ltd. (iSDA) at 30 m pixel size using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples.
Further information can be found in the FAQ and technical information documentation. To submit an issue or request support, please visit the iSDAsoil site.
0-20 厘米和 20-50 厘米土壤深度处的可提取铝，预测平均值和标准偏差。
像素值必须使用 exp(x/10)-1 进行反向转换。
决策农业创新解决方案有限公司 (iSDA) 使用机器学习、遥感数据和超过 100,000 个分析土壤样本的训练集，以 30 m 像素大小对土壤特性进行了预测。
更多信息可以在常见问题和技术信息文档中找到。要提交问题或请求支持，请访问 iSDAsoil 站点。
在茂密的丛林地区（通常在非洲中部），模型精度较低，因此可能会看到条带（条纹）等伪影。
决策农业创新解决方案有限公司 (iSDA) 使用机器学习、遥感数据和超过 100,000 个分析土壤样本的训练集，以 30 m 像素大小对土壤特性进行了预测。
更多信息可以在常见问题和技术信息文档中找到。要提交问题或请求支持，请访问 iSDAsoil 站点。
Dataset Availability
2001-01-01T00:00:00 - 2017-01-01T00:00:00
Dataset Provider
Collection Snippet
ee.Image("ISDASOIL/Africa/v1/aluminium_extractable")
Resolution
30 meters
Bands Table
| Name | Description | Min | Max | Units |
| mean_0_20 | Aluminium, extractable, predicted mean at 0-20 cm depth | 3 | 80 | ppm |
| mean_20_50 | Aluminium, extractable, predicted mean at 20-50 cm depth | 4 | 79 | ppm |
| stdev_0_20 | Aluminium, extractable, standard deviation at 0-20 cm depth | 1 | 53 | ppm |
| stdev_20_50 | Aluminium, extractable, standard deviation at 20-50 cm depth | 1 | 51 | ppm |
数据引用：
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
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
代码：
var mean_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' + '<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' + '<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' + '<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' + '<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' + '<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' + '<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' + '<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' + '<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var stdev_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' + '<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="5"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; Map.setCenter(25, -3, 2); var raw = ee.Image("ISDASOIL/Africa/v1/aluminium_extractable"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Aluminium, extractable, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Aluminium, extractable, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Aluminium, extractable, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Aluminium, extractable, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); Map.addLayer( converted.select(0), {min: 0, max: 100}, "Aluminium, extractable, mean, 0-20 cm");
