Digital soil mapping dataset of soil organic carbon content in the Heihe river basin (2012)


According to the global soil map. Net standard, the 0-1m soil depth is divided into 5 layers: 0-5cm, 5-15cm, 15-30cm, 30-60cm and 60-100cm. According to the principle of soil landscape model, the spatial distribution data products of soil organic carbon content in different layers are produced by using the digital soil mapping method. The prediction method is mainly based on the soil landscape model. The basic theory of the model is the classic soil genesis theory. The model regards the soil as the product of the comprehensive effects of climate, topography, parent material, biology and time. This data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation, 91325301). Scope: Heihe River Basin;

Projection: WGS · 1984 · Albers;

Spatial resolution: 100M;

Data format: TIFF;

Data content: spatial distribution of soil organic carbon content

Prediction method: enhanced regression tree

Environmental variables: main soil forming factors


File naming and required software

The data is stored in grid TIF format and can be opened and read by remote sensing software such as ArcGIS and QGIS


Data Citations Data citation guideline What's data citation?
Cite as:

ZHANG Ganlin, SONG Xiaodong. Digital soil mapping dataset of soil organic carbon content in the Heihe river basin (2012). National Tibetan Plateau Data Center, 2017. DOI: 10.11888/Soil.tpdc.270590. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Song, X.D., Brus, D.J., Liu, F., Li, D.C., Zhao, Y.G., Yang, J.L., Zhang, G.L. (2016). Mapping soil organic carbon content by geographically weighted regression: A case study in the Heihe River Basin, China. Geoderma, 261, 11–22.( View Details | Bibtex)

2. Song, X.D., Brus, D.J., Liu, F., Li, D.C., Zhao, Y.G., Yang, J.L., Zhang, G.L. (2016). Mapping soil organic carbon content by geographically weighted regression: A case study in the Heihe River Basin, China. Geoderma, 261: 11–22.( View Details | Bibtex)

3. Yang, R.M., Zhang, G.L, Liu, F., Lu, Y.Y., Yang, F., Yang, F., Yang, M., Zhao, Y.G., Li, D.C. (2016). Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecological Indicators, 60, 870–878.( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.


Copyright & License

To respect the intellectual property rights, protect the rights of data authors,expand servglacials of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).


License: This work is licensed under an Attribution 4.0 International (CC BY 4.0)


Related Resources
Comments

Current page automatically show English comments Show comments in all languages

Download Follow
Keywords
Geographic coverage
East: 101.99 West: 97.07
South: 37.69 North: 42.69
Details
  • Temporal resolution: Yearly
  • Spatial resolution: 10m - 100m
  • File size: 464 MB
  • Views: 2,702
  • Downloads: 14
  • Access: Requestable
  • Temporal coverage: 2012-07-07 To 2013-01-06
  • Updated time: 2021-04-19
Contacts
: ZHANG Ganlin   SONG Xiaodong  

Distributor: National Tibetan Plateau Data Center

Email: data@itpcas.ac.cn

Export metadata