
- 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
0-20 厘米和 20-50 厘米土壤深度的总碳量,预测的平均值和标准差。
像素值必须使用 exp(x/10)-1
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/kg | 0 | 58 | 米 | 碳总量,预测平均值(0-20 厘米深度) |
mean_20_50 |
g/kg | 0 | 55 | 米 | 碳总量(20-50 厘米深度的预测平均值) |
stdev_0_20 |
g/kg | 0 | 151 | 米 | 碳总量,0-20 厘米深度的标准差 |
stdev_20_50 |
g/kg | 0 | 150 | 米 | 碳总量,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="#000004" label="0-2" opacity="1" quantity="11"/>' + '<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' + '<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' + '<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' + '<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' + '<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' + '<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' + '<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' + '<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' + '<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' + '<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' + '<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' + '<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-2" opacity="1" quantity="11"/>' + '<ColorMapEntry color="#0C0927" label="2-5.7" opacity="1" quantity="19"/>' + '<ColorMapEntry color="#231151" label="5.7-10" opacity="1" quantity="24"/>' + '<ColorMapEntry color="#410F75" label="10-12.5" opacity="1" quantity="26"/>' + '<ColorMapEntry color="#5F187F" label="12.5-13.9" opacity="1" quantity="27"/>' + '<ColorMapEntry color="#7B2382" label="13.9-15.4" opacity="1" quantity="28"/>' + '<ColorMapEntry color="#982D80" label="15.4-17.2" opacity="1" quantity="29"/>' + '<ColorMapEntry color="#B63679" label="17.2-19.1" opacity="1" quantity="30"/>' + '<ColorMapEntry color="#D3436E" label="19.1-21.2" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#EB5760" label="21.2-23.5" opacity="1" quantity="32"/>' + '<ColorMapEntry color="#F8765C" label="23.5-26.1" opacity="1" quantity="33"/>' + '<ColorMapEntry color="#FD9969" label="26.1-29" opacity="1" quantity="34"/>' + '<ColorMapEntry color="#FEBA80" label="29-32.1" opacity="1" quantity="35"/>' + '<ColorMapEntry color="#FDDC9E" label="32.1-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#FCFDBF" label="35.6-40" opacity="1" quantity="39"/>' + '</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="5"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' + '</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="5"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="6"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/carbon_total"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Carbon, total, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Carbon, total, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Carbon, total, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Carbon, total, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); var visualization = {min: 0, max: 60}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Carbon, total, mean, 0-20 cm");