iSDAsoil Clay Content

ISDASOIL/Africa/v1/clay_content
데이터 세트 제공
2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
데이터 세트 출처
Earth Engine 스니펫
ee.Image("ISDASOIL/Africa/v1/clay_content")
태그
아프리카 점토 isda 흙

설명

토양 깊이 0~20cm 및 20~50cm의 점토 함량, 예측된 평균 및 표준 편차 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 % 0 84 30m

점토 함량, 0~20cm 깊이에서 예측된 평균

mean_20_50 % 0 78 30m

점토 콘텐츠, 20~50cm 깊이에서 예측된 평균

stdev_0_20 % 0 90 30m

점토 함량, 0~20cm 깊이에서의 표준편차

stdev_20_50 % 0 90 30m

점토 함량, 20~50cm 깊이의 표준편차

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="#00204D" label="0-8" opacity="1" quantity="8"/>' +
  '<ColorMapEntry color="#002D6C" label="8-14" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#16396D" label="14-17" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#36476B" label="17-19" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#4B546C" label="19-21" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#5C616E" label="21-22" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#6C6E72" label="22-24" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#7C7B78" label="24-25" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#8E8A79" label="25-26" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#A09877" label="26-28" opacity="1" quantity="28"/>' +
  '<ColorMapEntry color="#B3A772" label="28-30" opacity="1" quantity="30"/>' +
  '<ColorMapEntry color="#C6B66B" label="30-31" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#DBC761" label="31-33" opacity="1" quantity="33"/>' +
  '<ColorMapEntry color="#F0D852" label="33-36" opacity="1" quantity="36"/>' +
  '<ColorMapEntry color="#FFEA46" label="36-70" opacity="1" quantity="40"/>' +
 '</ColorMap>' +
 '<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
 '<ColorMap type="ramp">' +
  '<ColorMapEntry color="#00204D" label="0-8" opacity="1" quantity="8"/>' +
  '<ColorMapEntry color="#002D6C" label="8-14" opacity="1" quantity="14"/>' +
  '<ColorMapEntry color="#16396D" label="14-17" opacity="1" quantity="17"/>' +
  '<ColorMapEntry color="#36476B" label="17-19" opacity="1" quantity="19"/>' +
  '<ColorMapEntry color="#4B546C" label="19-21" opacity="1" quantity="21"/>' +
  '<ColorMapEntry color="#5C616E" label="21-22" opacity="1" quantity="22"/>' +
  '<ColorMapEntry color="#6C6E72" label="22-24" opacity="1" quantity="24"/>' +
  '<ColorMapEntry color="#7C7B78" label="24-25" opacity="1" quantity="25"/>' +
  '<ColorMapEntry color="#8E8A79" label="25-26" opacity="1" quantity="26"/>' +
  '<ColorMapEntry color="#A09877" label="26-28" opacity="1" quantity="28"/>' +
  '<ColorMapEntry color="#B3A772" label="28-30" opacity="1" quantity="30"/>' +
  '<ColorMapEntry color="#C6B66B" label="30-31" opacity="1" quantity="31"/>' +
  '<ColorMapEntry color="#DBC761" label="31-33" opacity="1" quantity="33"/>' +
  '<ColorMapEntry color="#F0D852" label="33-36" opacity="1" quantity="36"/>' +
  '<ColorMapEntry color="#FFEA46" label="36-70" opacity="1" quantity="40"/>' +
 '</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/clay_content");
Map.addLayer(
    raw.select(0).sldStyle(mean_0_20), {},
    "Clay content, mean visualization, 0-20 cm");
Map.addLayer(
    raw.select(1).sldStyle(mean_20_50), {},
    "Clay content, mean visualization, 20-50 cm");
Map.addLayer(
    raw.select(2).sldStyle(stdev_0_20), {},
    "Clay content, stdev visualization, 0-20 cm");
Map.addLayer(
    raw.select(3).sldStyle(stdev_20_50), {},
    "Clay content, stdev visualization, 20-50 cm");

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

var visualization = {min: 0, max: 50};

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

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