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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

Data file naming and use method

The data is stored in grid TIF 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

ZHANG Ganlin, SONG Xiaodong. Digital soil mapping dataset of soil organic carbon content in the Heihe river basin (2012). A Big Earth Data Platform for Three Poles, 2017. doi: 10.11888/Soil.tpdc.270590. (Download the reference： RIS | Bibtex )

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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