什么是OKR:Google的目标管理方针(OKR目标管理)
654
2022-05-30
iSDAsoil Depth to Bedrock
Depth to bedrock at 0-200 cm depth, predicted mean and standard deviation.
Due to the potential cropland mask that was used for generating the data, many areas of exposed rock (where depth to bedrock would be 0 cm) have been masked out and therefore appear as nodata values. The maximum depth of this layer is 200 cm, but this does not represent the maximum possible soil depth, therefore values of 200 should be interpreted as >= 200.
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.
0-200 厘米深度的基岩深度,预测平均值和标准偏差。 由于用于生成数据的潜在农田掩膜,许多裸露岩石区域(基岩深度为 0 厘米)已被掩蔽,因此显示为无数据值。该层的最大深度为 200 厘米,但这并不代表可能的最大土壤深度,因此值 200 应解释为 >= 200。 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。 Innovative Solutions for Decision Agriculture Ltd. (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 像素大小进行了土壤特性预测。 可以在常见问题解答和技术信息文档中找到更多信息。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/bedrock_depth")
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_200 = '
机器学习
版权声明:本文内容由网络用户投稿,版权归原作者所有,本站不拥有其著作权,亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容,请联系我们jiasou666@gmail.com 处理,核实后本网站将在24小时内删除侵权内容。