- 데이터 세트 사용 가능 기간
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- 데이터 세트 제공업체
- iSDA
- 태그
설명
0~20cm 및 20~50cm 토양 깊이에서 추출 가능한 알루미늄, 예측된 평균 및 표준 편차
픽셀 값은 exp(x/10)-1로 역변환해야 합니다.
토양 속성 예측은 Innovative Solutions for Decision Agriculture Ltd. (iSDA)에서 머신러닝과 원격 감지 데이터, 분석된 100,000개 이상의 토양 샘플의 학습 세트를 사용하여 30m 픽셀 크기로 이루어졌습니다.
자세한 내용은 FAQ 및 기술 정보 문서를 참고하세요. 문제를 제출하거나 지원을 요청하려면 iSDAsoil 사이트를 방문하세요.
밀림이 우거진 지역 (일반적으로 중앙 아프리카)에서는 모델 정확도가 낮으므로 밴딩 (줄무늬)과 같은 아티팩트가 표시될 수 있습니다.
대역
픽셀 크기
30미터
대역
| 이름 | 단위 | 최소 | 최대 | 픽셀 크기 | 설명 |
|---|---|---|---|---|---|
mean_0_20 |
ppm | 3 | 80 | 미터 | 알루미늄, 추출 가능, 0~20cm 깊이에서 예측된 평균 |
mean_20_50 |
ppm | 4 | 79 | 미터 | 알루미늄, 추출 가능, 20~50cm 깊이에서 예측된 평균 |
stdev_0_20 |
ppm | 1 | 53 | 미터 | 알루미늄, 추출 가능, 0~20cm 깊이의 표준 편차 |
stdev_20_50 |
ppm | 1 | 51 | 미터 | 알루미늄, 추출 가능, 20~50cm 깊이의 표준 편차 |
이용약관
이용약관
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
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
DOI
Earth Engine으로 탐색하기
코드 편집기(JavaScript)
var mean_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' + '<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' + '<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' + '<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' + '<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#000004" label="0-21.2" opacity="1" quantity="31"/>' + '<ColorMapEntry color="#0C0927" label="21.2-35.6" opacity="1" quantity="36"/>' + '<ColorMapEntry color="#231151" label="35.6-53.6" opacity="1" quantity="40"/>' + '<ColorMapEntry color="#410F75" label="53.6-65.7" opacity="1" quantity="42"/>' + '<ColorMapEntry color="#5F187F" label="65.7-72.7" opacity="1" quantity="43"/>' + '<ColorMapEntry color="#7B2382" label="72.7-80.5" opacity="1" quantity="44"/>' + '<ColorMapEntry color="#982D80" label="80.5-89" opacity="1" quantity="45"/>' + '<ColorMapEntry color="#B63679" label="89-98.5" opacity="1" quantity="46"/>' + '<ColorMapEntry color="#D3436E" label="98.5-108.9" opacity="1" quantity="47"/>' + '<ColorMapEntry color="#EB5760" label="108.9-120.5" opacity="1" quantity="48"/>' + '<ColorMapEntry color="#F8765C" label="120.5-133.3" opacity="1" quantity="49"/>' + '<ColorMapEntry color="#FD9969" label="133.3-147.4" opacity="1" quantity="50"/>' + '<ColorMapEntry color="#FEBA80" label="147.4-163" opacity="1" quantity="51"/>' + '<ColorMapEntry color="#FDDC9E" label="163-199.3" opacity="1" quantity="53"/>' + '<ColorMapEntry color="#FCFDBF" label="199.3-1800" opacity="1" quantity="55"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var stdev_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#fde725" label="low" opacity="1" quantity="5"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' + '<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="5"/>' + '<ColorMapEntry color="#5dc962" label=" " opacity="1" quantity="9"/>' + '<ColorMapEntry color="#20908d" label=" " opacity="1" quantity="10"/>' + '<ColorMapEntry color="#3a528b" label=" " opacity="1" quantity="12"/>' + '<ColorMapEntry color="#440154" label="high" opacity="1" quantity="14"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; Map.setCenter(25, -3, 2); var raw = ee.Image("ISDASOIL/Africa/v1/aluminium_extractable"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Aluminium, extractable, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Aluminium, extractable, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Aluminium, extractable, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Aluminium, extractable, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); Map.addLayer( converted.select(0), {min: 0, max: 100}, "Aluminium, extractable, mean, 0-20 cm");