Google Earth Engine ——2001-2017年非洲土壤在 土壤深度为 0-20 厘米和 20-50 厘米的美国农业部质地等级数据,预测平均值和标准偏差

2024-02-02 09:10:30 浏览数 (1)

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);

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