iSDAsoil Extractable Iron

ISDASOIL/Africa/v1/iron_extractable
資料集開放期間
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
資料集產生者
Earth Engine 程式碼片段
ee.Image("ISDASOIL/Africa/v1/iron_extractable")
標記
非洲 ISDA 土壤

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。

在叢林茂密的區域 (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 萬個分析土壤樣本的訓練集,以 30 公尺的像素大小進行預測。

詳情請參閱常見問題技術資訊說明文件。如要提交問題或要求支援,請造訪iSDAsoil 網站

頻帶

波段

像素大小:30 公尺 (所有頻帶)

名稱 單位 最小值 最大值 像素大小 說明
mean_0_20 ppm 0 62 30 公尺

可萃取鐵,預測平均深度為 0 到 20 公分

mean_20_50 ppm 0 47 30 公尺

可萃取鐵的預測平均值 (深度 20 至 50 公分)

stdev_0_20 ppm 0 39 30 公尺

可萃取鐵,0 到 20 公分深度的標準差

stdev_20_50 ppm 0 39 30 公尺

鐵,可萃取,20-50 公分深度的標準差

使用條款

使用條款

CC-BY-4.0

參考資料

參考資料:
  • 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");
在程式碼編輯器開啟