iSDAsoil extractable Aluminium

ISDASOIL/Africa/v1/aluminium_extractable
数据集可用性
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
数据集提供程序
Earth Engine 代码段
ee.Image("ISDASOIL/Africa/v1/aluminium_extractable")

说明

土壤深度为 0-20 厘米和 20-50 厘米的可提取铝含量，预测的均值和标准差。

必须使用 exp(x/10)-1 将像素值转换回来。

Innovative Solutions for Decision Agriculture Ltd. (iSDA) 使用机器学习、遥感数据和包含 10 万多份已分析土壤样品的训练集，在 30 米像素大小的范围内预测了土壤性质。

如需了解详情，请参阅常见问题解答和技术信息文档。如需提交问题或请求支持，请访问 ISDAsoil 网站。

在茂密丛林地区（通常位于非洲中部），模型准确性较低，因此可能会出现条纹（条带）等伪影。

频段

像素大小
30 米

乐队

名称 单位 最小值 最大值 说明
mean_0_20 ppm 3 80

铝，可提取，预测的平均值（深度 0-20 厘米）
mean_20_50 ppm 4 79

铝，可提取，预测平均值（深度 20-50 厘米）
stdev_0_20 ppm 1 53

铝，可提取，深度 0-20 厘米的标准差
stdev_20_50 ppm 1 51

铝，可提取，深度 20-50 厘米的标准差

使用条款

使用条款

CC-BY-4.0

引用

引用：

  • Hengl, T., Miller, M.A.E., Križan, J., et al. 使用双尺度集成机器学习技术，以 30 米的空间分辨率绘制了非洲土壤性质和营养成分图。Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y

  • Hengl, T., Miller, M.A.E., Križan, J., et al. 使用双尺度集成机器学习技术，以 30 米的空间分辨率绘制了非洲土壤性质和营养成分图。Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y

DOI

使用 Earth Engine 进行探索

Code Editor (JavaScript)

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");
在代码编辑器中打开
土壤深度为 0-20 厘米和 20-50 厘米的可提取铝的预测均值和标准差。必须使用 exp(x/10)-1 对像素值进行反转换。Innovative Solutions for Decision Agriculture Ltd. (iSDA) 使用机器学习技术结合遥感数据和 …
ISDASOIL/Africa/v1/aluminium_extractable, africa,aluminium,isda,soil
2001-01-01T00:00:00Z/2017-01-01T00:00:00Z
-35.22 -31.46 37.98 57.08
Google Earth Engine