
- Disponibilidade de conjuntos de dados
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- Provedor de conjunto de dados
- iSDA
- Tags
Descrição
Conteúdo de silte em profundidades de 0 a 20 cm e 20 a 50 cm, média prevista e desvio padrão.
Os valores de pixel precisam ser transformados novamente com exp(x/10)-1
.
Em áreas de selva densa (geralmente na África Central), a acurácia do modelo é baixa e, portanto, podem aparecer artefatos como bandas (listras).
As previsões de propriedades do solo foram feitas pela Innovative Solutions for Decision Agriculture Ltd. (iSDA) com tamanho de pixel de 30 m usando machine learning e dados de sensoriamento remoto,além de um conjunto de treinamento de mais de 100.000 amostras de solo analisadas.
Mais informações podem ser encontradas no FAQ e na documentação de informações técnicas. Para enviar um problema ou pedir suporte, acesse o site do iSDAsoil.
Bandas
Tamanho do pixel
30 metros
Bandas
Nome | Unidades | Mín. | Máx. | Tamanho do pixel | Descrição |
---|---|---|---|---|---|
mean_0_20 |
% | 1 | 61 | metros | Conteúdo de silte, média prevista em profundidade de 0 a 20 cm |
mean_20_50 |
% | 0 | 62 | metros | Conteúdo de silte, média prevista na profundidade de 20 a 50 cm |
stdev_0_20 |
% | 0 | 38 | metros | Conteúdo de silte, desvio padrão na profundidade de 0 a 20 cm |
stdev_20_50 |
% | 0 | 38 | metros | Conteúdo de silte, desvio padrão na profundidade de 20 a 50 cm |
Termos de Uso
Termos de Uso
Citações
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
Explorar com o Earth Engine
Editor de código (JavaScript)
var mean_0_20 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#00204D" label="0-7" opacity="1" quantity="7"/>' + '<ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>' + '<ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>' + '<ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>' + '<ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>' + '<ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>' + '<ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>' + '<ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>' + '<ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>' + '<ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>' + '<ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>' + '<ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>' + '<ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>' + '<ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var mean_20_50 = '<RasterSymbolizer>' + '<ColorMap type="ramp">' + '<ColorMapEntry color="#00204D" label="0-7" opacity="1" quantity="7"/>' + '<ColorMapEntry color="#002D6C" label="7-9" opacity="1" quantity="9"/>' + '<ColorMapEntry color="#16396D" label="9-10" opacity="1" quantity="10"/>' + '<ColorMapEntry color="#36476B" label="10-11" opacity="1" quantity="11"/>' + '<ColorMapEntry color="#4B546C" label="11-12" opacity="1" quantity="12"/>' + '<ColorMapEntry color="#5C616E" label="12-13" opacity="1" quantity="13"/>' + '<ColorMapEntry color="#6C6E72" label="13-14" opacity="1" quantity="14"/>' + '<ColorMapEntry color="#7C7B78" label="14-15" opacity="1" quantity="15"/>' + '<ColorMapEntry color="#8E8A79" label="15-16" opacity="1" quantity="16"/>' + '<ColorMapEntry color="#A09877" label="16-17" opacity="1" quantity="17"/>' + '<ColorMapEntry color="#B3A772" label="17-18" opacity="1" quantity="18"/>' + '<ColorMapEntry color="#C6B66B" label="18-19" opacity="1" quantity="19"/>' + '<ColorMapEntry color="#DBC761" label="19-20" opacity="1" quantity="20"/>' + '<ColorMapEntry color="#F0D852" label="20-22" opacity="1" quantity="22"/>' + '<ColorMapEntry color="#FFEA46" label="22-70" opacity="1" quantity="24"/>' + '</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="4.19000000000005"/>' + '</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="4.19000000000005"/>' + '</ColorMap>' + '<ContrastEnhancement/>' + '</RasterSymbolizer>'; var raw = ee.Image("ISDASOIL/Africa/v1/silt_content"); Map.addLayer( raw.select(0).sldStyle(mean_0_20), {}, "Silt content, mean visualization, 0-20 cm"); Map.addLayer( raw.select(1).sldStyle(mean_20_50), {}, "Silt content, mean visualization, 20-50 cm"); Map.addLayer( raw.select(2).sldStyle(stdev_0_20), {}, "Silt content, stdev visualization, 0-20 cm"); Map.addLayer( raw.select(3).sldStyle(stdev_20_50), {}, "Silt content, stdev visualization, 20-50 cm"); var converted = raw.divide(10).exp().subtract(1); var visualization = {min: 0, max: 15}; Map.setCenter(25, -3, 2); Map.addLayer(converted.select(0), visualization, "Silt content, mean, 0-20 cm");