
- 数据集可用性
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- 数据集提供商
- iSDA
- 标签
说明
土壤深度为 0-20 厘米和 20-50 厘米时,<2 毫米部分的容重,预测的平均值和标准差。
像素值必须使用 x/100
进行反向转换。
在茂密丛林地区(通常位于非洲中部),模型准确度较低,因此可能会看到条带状伪影。
土壤属性预测由 Innovative Solutions for Decision Agriculture Ltd. (iSDA) 使用机器学习与遥感数据以及超过 10 万个已分析土壤样本的训练集相结合,以 30 米像素大小进行。
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。
频段
像素大小
30 米
频段
名称 | 单位 | 最小值 | 最大值 | 像素尺寸 | 说明 |
---|---|---|---|---|---|
mean_0_20 |
g/cm^3 | 44 | 197 | 米 | 散装密度,<2 毫米级分,0-20 厘米深度的预测平均值 |
mean_20_50 |
g/cm^3 | 44 | 196 | 米 | 容重,<2 毫米部分,20-50 厘米深度的预测平均值 |
stdev_0_20 |
g/cm^3 | 0 | 92 | 米 | 容重,<2mm 部分,0-20 厘米深度的标准差 |
stdev_20_50 |
g/cm^3 | 0 | 92 | 米 | 容重(小于 2 毫米的粒径部分),20-50 厘米深度的标准差 |
使用条款
使用条款
引用
引用:
Hengl, T.、Miller, M.A.E.,Križan, J. 等人。使用双尺度集成机器学习技术,以 30 米的空间分辨率绘制非洲土壤属性和养分地图。Sci Rep 11, 6130 (2021). doi:10.1038/s41598-021-85639-y
使用 Earth Engine 探索
代码编辑器 (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");