Skip to main content
Log in

Mapping detailed soil property using small scale soil type maps and sparse typical samples

  • Published:
Chinese Geographical Science Aims and scope Submit manuscript

Abstract

Soil type maps at the scale of 1:1 000 000 are used extensively to provide soil spatial distribution information for soil erosion assessment and watershed management models in China. However, the soil property maps produced through conventional direct linking method usually suffer low accuracy as well as the lack of spatial details within a soil type polygon. This paper presents an effective method to produce detailed soil property map based on representative samples which were extracted from each polygon on the 1:1 000 000 soil type map. The representative sample of each polygon is defined as the location that can represent the largest area within the polygon. The representativeness of a candidate sample is determined by calculating the soil-forming environment condition similarities between the sample and other locations. Once the representative sample of each polygon has been chosen, the property values of the existing typical samples are assigned to the corresponding representative samples with the same soil type. Finally, based on these representative samples, the detailed soil property map could be produced by using existing digital soil mapping methods. The case study in XuanCheng City, Anhui Province of China, demonstrated the proposed method could produce soil property map at a higher level of spatial details and accuracy: 1) The soil organic matter (SOM) map produced based on the representative samples can not only depict the detailed spatial distribution of SOM within a soil type polygon but also largely reduce the abrupt change of soil property at the boundaries of two adjacent polygons. 2) The Root Mean Squared Error (RMSE) of the SOM map based on the representative samples is 1.61, and it is 1.37 for the SOM map produced by using conventional direct linking method. Therefore, the proposed method is an effective approach to produce spatial detailed soil property map with higher accuracy for environment simulation models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Band L E, Moore I D, 1995. Scale: Landscape attributes and geographical information systems. Hydrological Processes, 9(3–4): 401–422. doi: 10.1002/hyp.3360090312

    Article  Google Scholar 

  • Brus D J, Bogaert P, Heuvelink G B M, 2008. Bayesian maximum entropy prediction of soil categories using a traditional soil map as soft information. European Journal of Soil Science, 59(2): 166–177. doi: 10.1111/j.1365-2389.2007.00981.x

    Article  Google Scholar 

  • Chen L J, Zhu A X, Qin C Z et al., 2012. Effectiveness assessment of soil erosion critical source areas for soil and water conservation. Journal of Resources and Ecology, 3(2): 138–143. doi: 10.5814/j.issn.1674-764x.2012.02.005

    Article  Google Scholar 

  • Goovaerts P, 1999. Geostatistics in soil science: State-of-the-art and perspectives. Geoderma, 89(1–2): 1–45. doi: 10.1016/S0016-7061(98)00078-0

    Article  Google Scholar 

  • Hudson B D, 1992. The soil survey as paradigm-based science. Soil Science Society of America Journal, 56(3): 836–841. doi: 10.2136/sssaj1992.03615995005600030027x

    Article  Google Scholar 

  • Jenny H, 1941. Factors of Soil Formation-A System of Quantitative Pedology. New York: Dover Publications, 10–20.

    Google Scholar 

  • Kempen B, Brus D J, Heuvelink G B M et al., 2009. Updating the 1:50,000 Dutch soil map using legacy data: A multinomial logistic regression approach. Geoderma, 151(3–4): 311–326. doi: 10.1016/j.geoderma.2009.04.023

    Article  Google Scholar 

  • Li R K, Zhu A X, Song X F et al., 2012. Effects of spatial aggregation of soil spatial information on watershed hydrological modeling. Hydrological Processes, 26(9): 1390–1404. doi: 10.1002/hyp.8277

    Article  Google Scholar 

  • Liu Jing, Zhu Axing, Zhang Shujie et al., 2013. Mapping soil property over large area based on the individual representativeness of samples. Acta Pedologica Sinica, 50(1): 12–20. (in Chinese)

    Google Scholar 

  • Liu Y B, Batelaan O, de Smedt F et al., 2005. Test of a distributed modeling approach to predict flood flows in the Karst Suoimuoi catchment in Vietnam. Environmental Geology, 48(7): 931–940. doi: 10.1007/s00254-005-0031-1

    Article  Google Scholar 

  • McBratney A B, Mendonca S M L, Minasny B, 2003. On digital soil mapping. Geoderma, 117(1–2): 3–52. doi: 10.1016/S0016-7061(03)00223-4

    Article  Google Scholar 

  • McBratney A B, Odeh I O A, Bishop T F A et al., 2000. An overview of pedometric techniques for use in soil survey. Geoderma, 97(3–4): 293–327. doi: 10.1016/S0016-7061(00)00043-4

    Article  Google Scholar 

  • Moriasi D N, Starks P J, 2010. Effects of the resolution of soil dataset and precipitation dataset on SWAT2005 stream-flow calibration parameters and simulation accuracy. Journal of Soil and Water Conservation, 65(2): 163–178. doi: 10.2489/jswc.65.2.63

    Article  Google Scholar 

  • Mukundan R, Radcliffe D E, Risse L M, 2010. Spatial resolution of soil data and channel erosion effects on SWAT model predictions of flow and sediment. Journal of Soil and Water Conservation, 65(2): 92–104. doi: 10.2489/jswc.65.2.92

    Article  Google Scholar 

  • Qin C Z, Zhu A X, Pei T et al., 2007. An adaptive approach to selecting a flow-partition exponent for a multiple-flow-direction algorithm. International Journal of Geographical Information Science, 21(4): 443–458. doi: 10.1080/13658810601073240

    Article  Google Scholar 

  • Qin C Z, Zhu A X, Qiu W L et al., 2012. Mapping soil organic matter in small low-relief catchments using fuzzy slop position information. Geoderma, 171–172: 64–74. doi: 10.1016/j.geoderma.2011.06.006

    Article  Google Scholar 

  • Shi X, Zhu A X, Burt J E et al., 2004. A case-based reasoning approach to fuzzy soil mapping. Soil Science Society of America Journal, 68(3): 885–894. doi: 10.2136/sssaj2004.8850

    Article  Google Scholar 

  • Shi Xuezheng, Yu Dongsheng, Gao Peng et al., 2007. Soil information system of China (SISChina) and its application. Soils, 39(3): 329–333. (in Chinese)

    Google Scholar 

  • Sun Xiaolin, Zhao Yuguo, Qin Chengzhi et al., 2008. Effects of DEM resolution on multi-factor linear soil-landscape models and their application in predictive soil mapping. Acta Pedologica Sinica, 45(5): 971–977. (in Chinese)

    Google Scholar 

  • Vitharana U W A, Saey T, Cockx L et al., 2008. Upgrading a 1/20,000 soil map with an apparent electrical conductivity survey. Geoderma, 148(1): 107–112. doi: 10.1016/j.geoderma.2008.09.013

    Article  Google Scholar 

  • Yang L, Jiao Y, Fahmy S et al., 2012. Updating conventional soil maps using digital soil mapping. Soil Science Society of America Journal, 75(3): 1044–1053. doi: 10.2136/sssaj2010.0002

    Article  Google Scholar 

  • Yang Lin, Fahmy Sherif, Jiao You et al., 2010. Updating conventional soil maps using knowledge on soil-environment relationships extracted from the maps. Acta Pedologica Sinica, 47(6): 1039–1047. (in Chinese)

    Google Scholar 

  • Yu D S, Shi X Z, Wang H J et al., 2007. Regional patterns of soil organic carbon stocks in China. Journal of Environmental Management, 85(3): 680–689. doi: 10.1016/j.jenvman.2006.09.020

    Article  Google Scholar 

  • Yu Wanli, 2012. Soil Property Mapping on a National Scale Based on Sparse Grid Samples. Beijing: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 34–39. (in Chinese)

    Google Scholar 

  • Zhang Shujie, Zhu Axing, Liu Jing et al., 2012. Sample-based digital soil mapping methods and related sampling scheme. Soils, 44(6): 917–923. (in Chinese)

    Google Scholar 

  • Zhang Yong, Shi Xuezheng, Yu Dongsheng et al., 2008. Effects of the linkage between spatial data and attribute data on estimates of soil organic carbon. Advances in Earth Science, 23(8): 840–847. (in Chinese)

    Google Scholar 

  • Zhao Liang, Zhao Yuguo, Li Decheng et al., 2007. Digital soil mapping by extracting quantitative relationships between soil properties and terrain factors based on fuzzy set theory. Acta Pedologica Sinica, 44(6): 961–967. (in Chinese)

    Google Scholar 

  • Zhao Y C, Shi X Z, Weindorf D C et al., 2006. Map scale effects on soil organic carbon stock estimation in North China. Soil Science Society of America Journal, 70(4): 1377–1386. doi: 10.2136/sssaj2004.0165

    Article  Google Scholar 

  • Zhu A X, Band L E, Dutton B et al., 1996. Automated soil inference under fuzzy logic. Ecological Modelling, 90(2): 123–145. doi: 10.1016/0304-3800(95)00161-1

    Article  Google Scholar 

  • Zhu A X, 1997. A similarity model for representing soil spatial information. Geoderma, 77(2–4): 217–242. doi: 10.1016/S0016-7061(97)00023-2

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenliang Liu.

Additional information

Foundation item: Under the auspices of Program of International Science & Technology Cooperation, Ministry of Science and Technology of China (No. 2010DFB24140), National Natural Science Foundation of China (No. 41023010, 41001298), National High Technology Research and Development Program of China (No. 2011AA120305)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, S., Zhu, A., Liu, W. et al. Mapping detailed soil property using small scale soil type maps and sparse typical samples. Chin. Geogr. Sci. 23, 680–691 (2013). https://doi.org/10.1007/s11769-013-0632-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11769-013-0632-7

Keywords

Navigation