USDA Texture Class at soil depths of 0-20 cm and 20-50 cm.
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.
代码语言:javascript复制土壤深度为 0-20 厘米和 20-50 厘米的美国农业部质地等级。
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/texture_class")
Resolution
30 meters
Bands Table
Name | Description |
---|---|
texture_0_20 | USDA Texture Class at 0-20 cm depth |
texture_20_50 | USDA Texture Class at 20-50 cm depth |
Class Table: texture_0_20
Value | Color | Color Value | Description |
---|---|---|---|
1 | #d5c36b | Clay | |
2 | #b96947 | Silty Clay | |
3 | #9d3706 | Sandy Clay | |
4 | #ae868f | Clay Loam | |
5 | #f86714 | Silty Clay Loam | |
6 | #46d143 | Sandy Clay Loam | |
7 | #368f20 | Loam | |
8 | #3e5a14 | Silt Loam | |
9 | #ffd557 | Sandy Loam | |
10 | #fff72e | Silt | |
11 | #ff5a9d | Loamy Sand | |
12 | #ff005b | Sand |
Class Table: texture_20_50
Value | Color | Color Value | Description |
---|---|---|---|
1 | #d5c36b | Clay | |
2 | #b96947 | Silty Clay | |
3 | #9d3706 | Sandy Clay | |
4 | #ae868f | Clay Loam | |
5 | #f86714 | Silty Clay Loam | |
6 | #46d143 | Sandy Clay Loam | |
7 | #368f20 | Loam | |
8 | #3e5a14 | Silt Loam | |
9 | #ffd557 | Sandy Loam | |
10 | #fff72e | Silt | |
11 | #ff5a9d | Loamy Sand | |
12 | #ff005b | Sand |
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:
代码语言:javascript复制var raw = ee.Image("ISDASOIL/Africa/v1/texture_class");
Map.addLayer(
raw.select(0), {}, "Texture class, 0-20 cm");
Map.addLayer(
raw.select(1), {}, "Texture class, 20-50 cm");
Map.setCenter(25, -3, 2);