Chinese Geographical Science ›› 2017, Vol. 27 ›› Issue (4): 516-528.doi: 10.1007/s11769-017-0869-7

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Mapping Soil Organic Carbon Stocks of Northeastern China Using Expert Knowledge and GIS-based Methods

SONG Xiaodong, LIU Feng, JU Bing, ZHI Junjun, LI Decheng, ZHAO Yuguo, ZHANG Ganlin   

  1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
  • Received:2016-07-21 Revised:2016-11-05 Online:2017-08-27 Published:2017-06-30
  • Contact: ZHANG
  • Supported by:

    Under the auspices of Basic Project of State Commission of Science Technology of China (No. 2008FY110600), National Natural Science Foundation of China (No. 91325301, 41401237, 41571212, 41371224), Field Frontier Program of Institute of Soil Science, Chinese Academy of Sciences (No. ISSASIP1624)


The main aim of this paper was to calculate soil organic carbon stock (SOCS) with consideration of the pedogenetic horizons using expert knowledge and GIS-based methods in northeastern China. A novel prediction process was presented and was referred to as model-then-calculate with respect to the variable thicknesses of soil horizons (MCV). The model-then-calculate with fixed-thickness (MCF), soil profile statistics (SPS), pedological professional knowledge-based (PKB) and vegetation type-based (Veg) methods were carried out for comparison. With respect to the similar pedological information, nine common layers from topsoil to bedrock were grouped in the MCV. Validation results suggested that the MCV method generated better performance than the other methods considered. For the comparison of polygon based approaches, the Veg method generated better accuracy than both SPS and PKB, as limited soil data were incorporated. Additional prediction of the pedogenetic horizons within MCV benefitted the regional SOCS estimation and provided information for future soil classification and understanding of soil functions. The intermediate product, that is, horizon thickness maps were fluctuant enough and reflected many details in space. The linear mixed model indicated that mean annual air temperature (MAAT) was the most important predictor for the SOCS simulation. The minimal residual of the linear mixed models was achieved in the vegetation type-based model, whereas the maximal residual was fitted in the soil type-based model. About 95% of SOCS could be found in Argosols, Cambosols and Isohumosols. The largest SOCS was found in the croplands with vegetation of Triticum aestivum L., Sorghum bicolor (L.) Moench, Glycine max (L.) Merr., Zea mays L. and Setaria italica (L.) P. Beauv.

Key words: soil organic carbon stock, model-then-calculate, random forest, linear mixed model, northeastern China