
- 資料集可用性
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- 資料集來源
- iSDA
- 標記
說明
土壤深度 0-20 公分和 20-50 公分處的有效陽離子交換容量預測平均值和標準差,
像素值必須使用 exp(x/10)-1
進行反向轉換。
在叢林密布的區域 (通常位於中非),模型準確度較低,因此可能會出現帶狀 (條紋) 等構件。
土壤性質預測是由 Innovative Solutions for Decision Agriculture Ltd. (iSDA) 進行,採用機器學習技術搭配遙測資料,以及超過 10 萬個分析過的土壤樣本訓練集,以 30 公尺的像素大小進行預測。
詳情請參閱常見問題和技術資訊說明文件。如要提交問題或要求支援,請前往iSDAsoil 網站。
頻帶
像素大小
30 公尺
頻帶
名稱 | 單位 | 最小值 | 最大值 | 像素大小 | 說明 |
---|---|---|---|---|---|
mean_0_20 |
cmol(+)/kg | 0 | 45 | 公尺 | 有效陽離子交換容量,預測平均值 (深度 0 到 20 公分) |
mean_20_50 |
cmol(+)/kg | 0 | 46 | 公尺 | 有效陽離子交換容量,預測平均值 (深度 20 至 50 公分) |
stdev_0_20 |
cmol(+)/kg | 0 | 19 | 公尺 | 有效陽離子交換容量,0 到 20 公分深度的標準差 |
stdev_20_50 |
cmol(+)/kg | 0 | 20 | 公尺 | 有效陽離子交換容量,20 至 50 公分深度的標準差 |
使用條款
使用條款
引用內容
引用內容:
Hengl, T.、Miller, M.A.E.、Križan, J. 等人。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
使用 Earth Engine 探索
程式碼編輯器 (JavaScript)
var mean_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-3.5" opacity="1" quantity="15"/>' + '<ColorMapEntry color="#0C0927" label="3.5-4.5" opacity="1" quantity="17"/>' + '<ColorMapEntry color="#231151" label="4.5-5" opacity="1" quantity="18"/>' + '<ColorMapEntry color="#410F75" label="5-6.4" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#5F187F" label="6.4-7.2" opacity="1" quantity="21"/>' + '<ColorMapEntry color="#7B2382" label="7.2-8" opacity="1" quantity="22"/>' + '<ColorMapEntry color="#982D80" label="8-9" opacity="1" quantity="23"/>' + '<ColorMapEntry color="#B63679" label="9-10" opacity="1" quantity="24"/>' + '<ColorMapEntry color="#D3436E" label="10-11.2" opacity="1" quantity="25"/>' + '<ColorMapEntry color="#EB5760" label="11.2-12.5" opacity="1" quantity="26"/>' + '<ColorMapEntry color="#F8765C" label="12.5-13.9" opacity="1" quantity="27"/>' + '<ColorMapEntry color="#FD9969" label="13.9-15.4" opacity="1" quantity="28"/>' + '<ColorMapEntry color="#FEBA80" label="15.4-17.2" opacity="1" quantity="29"/>' + '<ColorMapEntry color="#FDDC9E" label="17.2-19.1" opacity="1" quantity="30"/>' + '<ColorMapEntry color="#FCFDBF" label="19.1-130" opacity="1" quantity="31"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-3.5" opacity="1" quantity="15"/>' + '<ColorMapEntry color="#0C0927" label="3.5-4.5" opacity="1" quantity="17"/>' + '<ColorMapEntry color="#231151" label="4.5-5" opacity="1" quantity="18"/>' + '<ColorMapEntry color="#410F75" label="5-6.4" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#5F187F" label="6.4-7.2" opacity="1" quantity="21"/>' + '<ColorMapEntry color="#7B2382" label="7.2-8" opacity="1" quantity="22"/>' + '<ColorMapEntry color="#982D80" label="8-9" opacity="1" quantity="23"/>' + '<ColorMapEntry color="#B63679" label="9-10" opacity="1" quantity="24"/>' + '<ColorMapEntry color="#D3436E" label="10-11.2" opacity="1" quantity="25"/>' + '<ColorMapEntry color="#EB5760" label="11.2-12.5" opacity="1" quantity="26"/>' + '<ColorMapEntry color="#F8765C" label="12.5-13.9" opacity="1" quantity="27"/>' + '<ColorMapEntry color="#FD9969" label="13.9-15.4" opacity="1" quantity="28"/>' + '<ColorMapEntry color="#FEBA80" label="15.4-17.2" opacity="1" quantity="29"/>' + '<ColorMapEntry color="#FDDC9E" label="17.2-19.1" opacity="1" quantity="30"/>' + '<ColorMapEntry color="#FCFDBF" label="19.1-130" opacity="1" quantity="31"/>' + '</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/cation_exchange_capacity"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Cation exchange capacity, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Cation exchange capacity, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Cation exchange capacity, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Cation exchange capacity, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); var visualization = {min: 0, max: 25}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Cation exchange capacity, mean, 0-20 cm");