
- 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 公分的可萃取鐵含量、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 |
ppm | 0 | 62 | 公尺 | 可萃取鐵,預測 0 到 20 公分深度的平均值 |
mean_20_50 |
ppm | 0 | 47 | 公尺 | 可萃取鐵,預測平均深度為 20 至 50 公分 |
stdev_0_20 |
ppm | 0 | 39 | 公尺 | 可萃取鐵,0 到 20 公分深度的標準差 |
stdev_20_50 |
ppm | 0 | 39 | 公尺 | 可萃取鐵,深度 20 至 50 公分處的標準差 |
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="#0D0887" label="0-6.4" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#350498" label="6.4-13.9" opacity="1" quantity="27"/>' + '<ColorMapEntry color="#5402A3" label="13.9-29" opacity="1" quantity="34"/>' + '<ColorMapEntry color="#7000A8" label="29-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#8B0AA5" label="35.6-43.7" opacity="1" quantity="38"/>' + '<ColorMapEntry color="#A31E9A" label="43.7-48.4" opacity="1" quantity="39"/>' + '<ColorMapEntry color="#B93289" label="48.4-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#CC4678" label="53.6-59.3" opacity="1" quantity="41"/>' + '<ColorMapEntry color="#DB5C68" label="59.3-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#E97158" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#F48849" label="72.7-80.5" opacity="1" quantity="44"/>' + '<ColorMapEntry color="#FBA139" label="80.5-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#FEBC2A" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#FADA24" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#F0F921" label="108.9-1200" opacity="1" quantity="48"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#0D0887" label="0-6.4" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#350498" label="6.4-13.9" opacity="1" quantity="27"/>' + '<ColorMapEntry color="#5402A3" label="13.9-29" opacity="1" quantity="34"/>' + '<ColorMapEntry color="#7000A8" label="29-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#8B0AA5" label="35.6-43.7" opacity="1" quantity="38"/>' + '<ColorMapEntry color="#A31E9A" label="43.7-48.4" opacity="1" quantity="39"/>' + '<ColorMapEntry color="#B93289" label="48.4-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#CC4678" label="53.6-59.3" opacity="1" quantity="41"/>' + '<ColorMapEntry color="#DB5C68" label="59.3-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#E97158" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#F48849" label="72.7-80.5" opacity="1" quantity="44"/>' + '<ColorMapEntry color="#FBA139" label="80.5-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#FEBC2A" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#FADA24" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#F0F921" label="108.9-1200" opacity="1" quantity="48"/>' + '</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="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="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="6"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/iron_extractable"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Iron, extractable, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Iron, extractable, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Iron, extractable, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Iron, extractable, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); var visualization = {min: 0, max: 140}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Iron, extractable, mean, 0-20 cm");