
- 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 公分的沙含量,\n 預測平均值和標準差。 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 |
% | 2 | 94 | 公尺 | 沙含量,預測平均值 (深度 0 到 20 公分) |
mean_20_50 |
% | 2 | 95 | 公尺 | 沙含量,預測平均深度為 20 至 50 公分 |
stdev_0_20 |
% | 0 | 144 | 公尺 | 沙子含量,0 到 20 公分深度的標準差 |
stdev_20_50 |
% | 0 | 143 | 公尺 | 沙子含量,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="#00204D" label="0-31" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#002D6C" label="31-39" opacity="1" quantity="39"/>' + '<ColorMapEntry color="#16396D" label="39-43" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#36476B" label="43-46" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#4B546C" label="46-49" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#5C616E" label="49-52" opacity="1" quantity="52"/>' + '<ColorMapEntry color="#6C6E72" label="52-54" opacity="1" quantity="54"/>' + '<ColorMapEntry color="#7C7B78" label="54-56" opacity="1" quantity="56"/>' + '<ColorMapEntry color="#8E8A79" label="56-58" opacity="1" quantity="58"/>' + '<ColorMapEntry color="#A09877" label="58-60" opacity="1" quantity="60"/>' + '<ColorMapEntry color="#B3A772" label="60-63" opacity="1" quantity="63"/>' + '<ColorMapEntry color="#C6B66B" label="63-65" opacity="1" quantity="65"/>' + '<ColorMapEntry color="#DBC761" label="65-68" opacity="1" quantity="68"/>' + '<ColorMapEntry color="#F0D852" label="68-71" opacity="1" quantity="71"/>' + '<ColorMapEntry color="#FFEA46" label="71-100" opacity="1" quantity="75"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#00204D" label="0-31" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#002D6C" label="31-39" opacity="1" quantity="39"/>' + '<ColorMapEntry color="#16396D" label="39-43" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#36476B" label="43-46" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#4B546C" label="46-49" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#5C616E" label="49-52" opacity="1" quantity="52"/>' + '<ColorMapEntry color="#6C6E72" label="52-54" opacity="1" quantity="54"/>' + '<ColorMapEntry color="#7C7B78" label="54-56" opacity="1" quantity="56"/>' + '<ColorMapEntry color="#8E8A79" label="56-58" opacity="1" quantity="58"/>' + '<ColorMapEntry color="#A09877" label="58-60" opacity="1" quantity="60"/>' + '<ColorMapEntry color="#B3A772" label="60-63" opacity="1" quantity="63"/>' + '<ColorMapEntry color="#C6B66B" label="63-65" opacity="1" quantity="65"/>' + '<ColorMapEntry color="#DBC761" label="65-68" opacity="1" quantity="68"/>' + '<ColorMapEntry color="#F0D852" label="68-71" opacity="1" quantity="71"/>' + '<ColorMapEntry color="#FFEA46" label="71-100" opacity="1" quantity="75"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var stdev_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="7"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var stdev_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="2"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="3"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="4"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="6"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="7"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/sand_content"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Sand content, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Sand content, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Sand content, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Sand content, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); var visualization = {min: 0, max: 3000}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Sand content, mean, 0-20 cm");