
- 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 公分的總氮含量、預測平均值和標準差。
像素值必須使用 exp(x/100)-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 |
g/kg | 3 | 246 | 公尺 | 氮總量,預測平均值 (0-20 公分深度) |
mean_20_50 |
g/kg | 0 | 254 | 公尺 | 氮總量,預測平均值 (深度 20 至 50 公分) |
stdev_0_20 |
g/kg | 1 | 124 | 公尺 | 氮總量,0 到 20 公分深度的標準差 |
stdev_20_50 |
g/kg | 1 | 125 | 公尺 | 氮總量,標準差 (深度 20 至 50 公分) |
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="0-0.2" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#0C0927" label="0.2-0.3" opacity="1" quantity="30"/>' + '<ColorMapEntry color="#231151" label="0.3-0.4" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#410F75" label="0.4-0.5" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#5F187F" label="0.5-0.6" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#7B2382" label="0.6-0.7" opacity="1" quantity="52"/>' + '<ColorMapEntry color="#982D80" label="0.7-0.8" opacity="1" quantity="58"/>' + '<ColorMapEntry color="#B63679" label="0.8-0.9" opacity="1" quantity="64"/>' + '<ColorMapEntry color="#D3436E" label="0.9-1" opacity="1" quantity="67"/>' + '<ColorMapEntry color="#EB5760" label="1-1.1" opacity="1" quantity="75"/>' + '<ColorMapEntry color="#F8765C" label="1.1-1.2" opacity="1" quantity="79"/>' + '<ColorMapEntry color="#FD9969" label="1.2-1.3" opacity="1" quantity="83"/>' + '<ColorMapEntry color="#FEBA80" label="1.3-1.4" opacity="1" quantity="89"/>' + '<ColorMapEntry color="#FDDC9E" label="1.4-1.5" opacity="1" quantity="93"/>' + '<ColorMapEntry color="#FCFDBF" label="1.5-10" opacity="1" quantity="99"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-0.2" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#0C0927" label="0.2-0.3" opacity="1" quantity="30"/>' + '<ColorMapEntry color="#231151" label="0.3-0.4" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#410F75" label="0.4-0.5" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#5F187F" label="0.5-0.6" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#7B2382" label="0.6-0.7" opacity="1" quantity="52"/>' + '<ColorMapEntry color="#982D80" label="0.7-0.8" opacity="1" quantity="58"/>' + '<ColorMapEntry color="#B63679" label="0.8-0.9" opacity="1" quantity="64"/>' + '<ColorMapEntry color="#D3436E" label="0.9-1" opacity="1" quantity="67"/>' + '<ColorMapEntry color="#EB5760" label="1-1.1" opacity="1" quantity="75"/>' + '<ColorMapEntry color="#F8765C" label="1.1-1.2" opacity="1" quantity="79"/>' + '<ColorMapEntry color="#FD9969" label="1.2-1.3" opacity="1" quantity="83"/>' + '<ColorMapEntry color="#FEBA80" label="1.3-1.4" opacity="1" quantity="89"/>' + '<ColorMapEntry color="#FDDC9E" label="1.4-1.5" opacity="1" quantity="93"/>' + '<ColorMapEntry color="#FCFDBF" label="1.5-10" opacity="1" quantity="99"/>' + '</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="8"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="14"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="60"/>' + '</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="8"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="14"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="60"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/nitrogen_total"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Nitrogen, total, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Nitrogen, total, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Nitrogen, total, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Nitrogen, total, stdev visualization, 20-50 cm"); var converted = raw.divide(100).exp().subtract(1); var visualization = {min: 0, max: 10000}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Nitrogen, total, mean, 0-20 cm");