Digital soil mapping dataset of soil texture in the Heihe river basin (2012-2014)


The American system classification is used as the standard of soil particle classification. The source data of 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 of Heihe River Basin, 91325301). 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.

Scope: Heihe River Basin;

Projection: WGS · 1984 · Albers;

Spatial resolution: 100M;

Data format: TIFF;

Data content: spatial distribution of soil clay, silt and sand content

Prediction method: enhanced regression tree

Environmental variables: main soil forming factors


Data file naming and use method

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


Required Data Citation View Data Cite Help About Data Citation
Cite as:

ZHANG Ganlin, SONG Xiaodong. Digital soil mapping dataset of soil texture in the Heihe river basin (2012-2014). A Big Earth Data Platform for Three Poles, 2017. doi: 10.11888/Soil.tpdc.270592. (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.


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


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Keywords
Geographic coverage
East: 101.99 West: 97.07
South: 37.69 North: 42.69
Detail
  • Temporal resolution: Yearly
  • Spatial resolution: 10m - 100m
  • File size: 472 MB
  • Browse count: 1,920 Times
  • Apply count: 4 Times
  • Share mode: offline
  • Temporal coverage: 2012-01-03 To 2015-01-02
  • Updated time:
Contact Information
: ZHANG Ganlin   SONG Xiaodong  

Distributor: A Big Earth Data Platform for Three Poles

Email: poles@itpcas.ac.cn

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