
- 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 厘米土壤深度的可提取钾,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 |
ppm | 1 | 80 | 米 | 钾(可提取),0-20 厘米深度的预测平均值 |
mean_20_50 |
ppm | 0 | 79 | 米 | 钾,可提取,20-50 厘米深度的预测平均值 |
stdev_0_20 |
ppm | 0 | 92 | 米 | 钾(可提取),0-20 厘米深度的标准差 |
stdev_20_50 |
ppm | 0 | 92 | 米 | 可提取钾,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="#0D0887" label="0-32.1" opacity="1" quantity="35"/>' + '<ColorMapEntry color="#350498" label="32.1-43.7" opacity="1" quantity="38"/>' + '<ColorMapEntry color="#5402A3" label="43.7-48.4" opacity="1" quantity="39"/>' + '<ColorMapEntry color="#7000A8" label="48.4-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#8B0AA5" label="53.6-59.3" opacity="1" quantity="41"/>' + '<ColorMapEntry color="#A31E9A" label="59.3-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#B93289" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#CC4678" label="72.7-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#DB5C68" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#E97158" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#F48849" label="108.9-120.5" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#FBA139" label="120.5-133.3" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#FEBC2A" label="133.3-163" opacity="1" quantity="51"/>' + '<ColorMapEntry color="#FADA24" label="163-199.3" opacity="1" quantity="53"/>' + '<ColorMapEntry color="#F0F921" label="163-1200" opacity="1" quantity="55"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#0D0887" label="0-32.1" opacity="1" quantity="35"/>' + '<ColorMapEntry color="#350498" label="32.1-43.7" opacity="1" quantity="38"/>' + '<ColorMapEntry color="#5402A3" label="43.7-48.4" opacity="1" quantity="39"/>' + '<ColorMapEntry color="#7000A8" label="48.4-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#8B0AA5" label="53.6-59.3" opacity="1" quantity="41"/>' + '<ColorMapEntry color="#A31E9A" label="59.3-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#B93289" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#CC4678" label="72.7-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#DB5C68" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#E97158" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#F48849" label="108.9-120.5" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#FBA139" label="120.5-133.3" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#FEBC2A" label="133.3-163" opacity="1" quantity="51"/>' + '<ColorMapEntry color="#FADA24" label="163-199.3" opacity="1" quantity="53"/>' + '<ColorMapEntry color="#F0F921" label="163-1200" opacity="1" quantity="55"/>' + '</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="2"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>' + '</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="2"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="3"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="4"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="5"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/potassium_extractable"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Potassium extractable, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Potassium extractable, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Potassium extractable, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Potassium extractable, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); var visualization = {min: 0, max: 250}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Potassium extractable, mean, 0-20 cm");