
- Disponibilidade de conjuntos de dados
- 2001-01-01T00:00:00Z–2017-01-01T00:00:00Z
- Provedor de conjunto de dados
- iSDA
- Tags
Descrição
Ferro extraível em profundidades de solo de 0 a 20 cm e de 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 |
ppm | 0 | 62 | metros | Ferro extraível, média prevista em profundidade de 0 a 20 cm |
mean_20_50 |
ppm | 0 | 47 | metros | Ferro extraível, média prevista a uma profundidade de 20 a 50 cm |
stdev_0_20 |
ppm | 0 | 39 | metros | Ferro extraível, desvio padrão a uma profundidade de 0 a 20 cm |
stdev_20_50 |
ppm | 0 | 39 | metros | Ferro extraível, 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="#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");