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Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取粘土含量,预测平均值和标准偏差数据集


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

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 厘米的粘土含量,预测平均值和标准偏差。 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/clay_content")​

Resolution

30 meters

Bands Table

Name

Description

Min

Max

Units

mean_0_20

Clay content, predicted mean at 0-20 cm depth

0

84

%

mean_20_50

Clay content, predicted mean at 20-50 cm depth

0

78

%

stdev_0_20

Clay content, standard deviation at 0-20 cm depth

0

90

%

stdev_20_50

Clay content, standard deviation at 20-50 cm depth

0

90

%

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 =
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'';

var mean_20_50 =
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'';

var stdev_0_20 =
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'';

var stdev_20_50 =
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'' +
'';

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");

Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取粘土含量,预测平均值和标准偏差数据集_2d

 Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取粘土含量,预测平均值和标准偏差数据集_神经网络_02

 Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取粘土含量,预测平均值和标准偏差数据集_自动驾驶_03

 Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取粘土含量,预测平均值和标准偏差数据集_深度学习_04

 Google Earth Engine ——2001-2017年非洲土壤深度 0-20 厘米和 20-50 厘米的可提取粘土含量,预测平均值和标准偏差数据集_2d_05


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