0
点赞
收藏
分享

微信扫一扫

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


Extractable zinc 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 0-20 厘米和 20-50 厘米的可提取锌,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/zinc_extractable")​

Resolution

30 meters

Bands Table

Name

Description

Min

Max

Units

mean_0_20

Zinc, extractable, predicted mean at 0-20 cm depth

1

32

ppm

mean_20_50

Zinc, extractable, predicted mean at 20-50 cm depth

0

31

ppm

stdev_0_20

Zinc, extractable, standard deviation at 0-20 cm depth

0

11

ppm

stdev_20_50

Zinc, extractable, standard deviation at 20-50 cm depth

0

10

ppm

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 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-0.6" opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#350498" label="0.6-0.8" opacity="1" quantity="6"/>' +
'<ColorMapEntry color="#5402A3" label="0.8-1" opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#7000A8" label="1-1.2" opacity="1" quantity="8"/>' +
'<ColorMapEntry color="#8B0AA5" label="1.2-1.5" opacity="1" quantity="9"/>' +
'<ColorMapEntry color="#A31E9A" label="1.5-1.7" opacity="1" quantity="10"/>' +
'<ColorMapEntry color="#B93289" label="1.7-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#CC4678" label="2-2.3" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#DB5C68" label="2.3-2.7" opacity="1" quantity="13"/>' +
'<ColorMapEntry color="#E97158" label="2.7-3.1" opacity="1" quantity="14"/>' +
'<ColorMapEntry color="#F48849" label="3.1-3.5" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#FBA139" label="3.5-4" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#FEBC2A" label="4-4.5" opacity="1" quantity="17"/>' +
'<ColorMapEntry color="#FADA24" label="4.5-5" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#F0F921" label="5-125" opacity="1" quantity="19"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';

var mean_20_50 =
'<RasterSymbolizer>' +
'<ColorMap type="ramp">' +
'<ColorMapEntry color="#0D0887" label="0-0.6" opacity="1" quantity="5"/>' +
'<ColorMapEntry color="#350498" label="0.6-0.8" opacity="1" quantity="6"/>' +
'<ColorMapEntry color="#5402A3" label="0.8-1" opacity="1" quantity="7"/>' +
'<ColorMapEntry color="#7000A8" label="1-1.2" opacity="1" quantity="8"/>' +
'<ColorMapEntry color="#8B0AA5" label="1.2-1.5" opacity="1" quantity="9"/>' +
'<ColorMapEntry color="#A31E9A" label="1.5-1.7" opacity="1" quantity="10"/>' +
'<ColorMapEntry color="#B93289" label="1.7-2" opacity="1" quantity="11"/>' +
'<ColorMapEntry color="#CC4678" label="2-2.3" opacity="1" quantity="12"/>' +
'<ColorMapEntry color="#DB5C68" label="2.3-2.7" opacity="1" quantity="13"/>' +
'<ColorMapEntry color="#E97158" label="2.7-3.1" opacity="1" quantity="14"/>' +
'<ColorMapEntry color="#F48849" label="3.1-3.5" opacity="1" quantity="15"/>' +
'<ColorMapEntry color="#FBA139" label="3.5-4" opacity="1" quantity="16"/>' +
'<ColorMapEntry color="#FEBC2A" label="4-4.5" opacity="1" quantity="17"/>' +
'<ColorMapEntry color="#FADA24" label="4.5-5" opacity="1" quantity="18"/>' +
'<ColorMapEntry color="#F0F921" label="5-125" opacity="1" quantity="19"/>' +
'</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="5"/>' +
'</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="5"/>' +
'</ColorMap>' +
'<ContrastEnhancement/>' +
'</RasterSymbolizer>';

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

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

var visualization = {min: 0, max: 10};

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

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

Google Earth Engine ——2001-2017年非洲土壤在土壤深度 0-20 厘米和 20-50 厘米的可提取锌,预测平均值和标准偏差数据_css

 Google Earth Engine ——2001-2017年非洲土壤在土壤深度 0-20 厘米和 20-50 厘米的可提取锌,预测平均值和标准偏差数据_非洲_02

 Google Earth Engine ——2001-2017年非洲土壤在土壤深度 0-20 厘米和 20-50 厘米的可提取锌,预测平均值和标准偏差数据_锌_03

 Google Earth Engine ——2001-2017年非洲土壤在土壤深度 0-20 厘米和 20-50 厘米的可提取锌,预测平均值和标准偏差数据_html_04

 Google Earth Engine ——2001-2017年非洲土壤在土壤深度 0-20 厘米和 20-50 厘米的可提取锌,预测平均值和标准偏差数据_锌_05


举报

相关推荐

0 条评论