Google Earth Engine ——2001-2017年非洲土壤在 0-20 厘米和 20-50 厘米的土壤深度处可提取的石含量数据，预测平均值和标准偏差

Stone content  at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation.

Pixel values must be back-transformed with ​​exp(x/10)-1​​.

In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artefacts such as banding (striping) might be seen.

Soil property predictions were made by ​​Innovative Solutions for Decision Agriculture Ltd. (iSDA)​​ at 30 m pixel size using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples.

Further information can be found in the ​​FAQ​​​ and ​​technical information documentation​​​. To submit an issue or request support, please visit ​​the iSDAsoil site​​.

土壤深度为 0-20 厘米和 20-50 厘米的可提取含石量，预测平均值和标准偏差。 像素值必须使用 exp(x/10)-1 进行反向转换。 在茂密的丛林地区（通常在非洲中部），模型精度较低，因此可能会看到条带（条纹）等伪影。 决策农业创新解决方案有限公司 (iSDA) 使用机器学习、遥感数据和超过 100,000 个分析土壤样本的训练集，以 30 m 像素大小对土壤特性进行了预测。 更多信息可以在常见问题和技术信息文档中找到。要提交问题或请求支持，请访问 iSDAsoil 站点。

Dataset Availability

2001-01-01T00:00:00 - 2017-01-01T00:00:00

Dataset Provider

​​iSDA​​

Collection Snippet

ee.Image("ISDASOIL/Africa/v1/stone_content")

Resolution

30 meters

Bands Table

数据引用：

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​​

代码：

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

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

var visualization = {min: 0, max: 6};

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

Map.addLayer(converted.select(0), visualization, "Stone content, mean, 0-20 cm");