iSDAsoil Extractable Sulfur

ISDASOIL/Africa/v1/sulphur_extractable
데이터 세트 제공
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
데이터 세트 출처
Earth Engine 스니펫
ee.Image("ISDASOIL/Africa/v1/sulphur_extractable")
태그
africa isda soil
황

설명

토양 깊이 0~20cm 및 20~50cm에서 추출 가능한 황, 예측 평균 및 표준 편차

픽셀 값은 exp(x/10)-1로 역변환해야 합니다.

밀림이 많은 지역 (일반적으로 중앙 아프리카)에서는 모델 정확도가 낮으므로 밴딩 (줄무늬)과 같은 아티팩트가 표시될 수 있습니다.

토양 속성 예측은 Innovative Solutions for Decision Agriculture Ltd. (iSDA)에서 머신러닝과 원격 감지 데이터, 분석된 100,000개 이상의 토양 샘플 학습 세트를 사용하여 30m 픽셀 크기로 이루어졌습니다.

자세한 내용은 FAQ 및 기술 정보 문서를 참고하세요. 문제를 제출하거나 지원을 요청하려면 iSDAsoil 사이트를 방문하세요.

대역

대역

픽셀 크기: 30미터 (모든 밴드)

이름 단위 최소 최대 픽셀 크기 설명
mean_0_20 ppm 3 42 30m

추출 가능한 황, 0~20cm 깊이에서 예측된 평균

mean_20_50 ppm 0 39 30m

추출 가능한 황, 20~50cm 깊이에서 예측된 평균

stdev_0_20 ppm 1 37 30m

황, 추출 가능, 0~20cm 깊이에서의 표준 편차

stdev_20_50 ppm 3 41 30m

황, 추출 가능, 20~50cm 깊이의 표준 편차

이용약관

이용약관

CC-BY-4.0

인용

인용:
  • Hengl, T., Miller, M.A.E., Križan, J., et al. 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

Earth Engine으로 탐색

코드 편집기(JavaScript)

var mean_0_20 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#0D0887" label="0-2.3" opacity="1" quantity="12"/>' +
  '<ColorMapEntry color="#350498" label="2.3-3.1" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#5402A3" label="3.1-3.5" opacity="1" quantity="15"/>' +
  '<ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>' +
  '<ColorMapEntry color="#8B0AA5" label="4-5" opacity="1" quantity="18"/>' +
  '<ColorMapEntry color="#A31E9A" label="5-5.7" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#B93289" label="5.7-6.4" opacity="1" quantity="20"/>' +
  '<ColorMapEntry color="#CC4678" label="6.4-7.2" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#DB5C68" label="7.2-8" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#E97158" label="8-9" opacity="1" quantity="23"/>' +
  '<ColorMapEntry color="#F48849" label="9-10" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#FBA139" label="10-11.2" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#FEBC2A" label="11.2-12.5" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#FADA24" label="12.5-15.4" opacity="1" quantity="28"/>' +
  '<ColorMapEntry color="#F0F921" label="15.4-125" opacity="1" quantity="30"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#0D0887" label="0-2.3" opacity="1" quantity="12"/>' +
  '<ColorMapEntry color="#350498" label="2.3-3.1" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#5402A3" label="3.1-3.5" opacity="1" quantity="15"/>' +
  '<ColorMapEntry color="#7000A8" label="3.5-4" opacity="1" quantity="16"/>' +
  '<ColorMapEntry color="#8B0AA5" label="4-5" opacity="1" quantity="18"/>' +
  '<ColorMapEntry color="#A31E9A" label="5-5.7" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#B93289" label="5.7-6.4" opacity="1" quantity="20"/>' +
  '<ColorMapEntry color="#CC4678" label="6.4-7.2" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#DB5C68" label="7.2-8" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#E97158" label="8-9" opacity="1" quantity="23"/>' +
  '<ColorMapEntry color="#F48849" label="9-10" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#FBA139" label="10-11.2" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#FEBC2A" label="11.2-12.5" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#FADA24" label="12.5-15.4" opacity="1" quantity="28"/>' +
  '<ColorMapEntry color="#F0F921" label="15.4-125" opacity="1" quantity="30"/>' +
 '</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="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="14"/>' +
 '</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="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="14"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var raw = ee.Image("ISDASOIL/Africa/v1/sulphur_extractable");
Map.addLayer(
    raw.select(0).sldStyle(mean_0_20), {},
    "Sulphur extractable, mean visualization, 0-20 cm");
Map.addLayer(
    raw.select(1).sldStyle(mean_20_50), {},
    "Sulphur extractable, mean visualization, 20-50 cm");
Map.addLayer(
    raw.select(2).sldStyle(stdev_0_20), {},
    "Sulphur extractable, stdev visualization, 0-20 cm");
Map.addLayer(
    raw.select(3).sldStyle(stdev_20_50), {},
    "Sulphur extractable, stdev visualization, 20-50 cm");

var converted = raw.divide(10).exp().subtract(1);

var visualization = {min: 0, max: 20};

Map.setCenter(25, -3, 2);

Map.addLayer(converted.select(0), visualization, "Sulphur extractable, mean, 0-20 cm");
코드 편집기에서 열기