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Spatial Prediction of Soil Organic Matter Using Geostatistics and Topographic Unit Zoning Integrated in GIS: A Case Study
Earth Sciences
Volume 8, Issue 5, October 2019, Pages: 294-302
Received: May 30, 2019; Accepted: Oct. 22, 2019; Published: Oct. 28, 2019
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Zhou Ziyan, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Fu Peihong, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Han Zongwei, Department of Tourism and Geography, Tongren University, Tongren, Guizhou, China
Huang Wei, College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
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The spatial distribution of soil organic matter (SOM) has a close connection with topography. To understand the effects of topographic synergy effects in traditional geostatistic methods, the influence of topography is considered in SOM geostatistic studies by combining geographic unit zoning and spatial prediction. We explored the changes in the SOM distribution between that obtained using spatial interpolation integrated with 13 different classical topographic units and determined using global interpolation with 6485 random soil samples obtained from Zhongxiang City, Hubei Province, China. The steps are as follows. At first, the terrain factors were calculated from the digital elevation data (DEM) and the topographic units were precisely divided into 13 different classical types more subtly by integrating the terrain factors. The regions were divided, which was based on terrain classification rules formed by the distribution of terrain factors in different landforms. Secondly, soil samples were collected in different topographic types, and the distribution of SOM for each sample set in different topographic units was generated by ordinary Kriging. Then, the corresponding results of interpolation for each sample set were segmented based on topographic unit region, and combining the result in each region, the spatial distribution of SOM based on topographic unit was obtained. Finally, verification and comparison with the accuracy of each SOM distributions were performed, which were obtained by using topography based geostatistics and traditional global geostatistics, respectively. Our results indicated that more accurate SOM spatial distributions can be obtained using the proposed method, especially in regions with gentle topography, such as ridge, shoulder, summit, toe slope (north/northeast side), and low-lying terrain units.
Soil Organic Matter, Geostatistics, Topographic Unit, Spatial Prediction
To cite this article
Zhou Ziyan, Fu Peihong, Han Zongwei, Huang Wei, Spatial Prediction of Soil Organic Matter Using Geostatistics and Topographic Unit Zoning Integrated in GIS: A Case Study, Earth Sciences. Vol. 8, No. 5, 2019, pp. 294-302. doi: 10.11648/
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Baveye, P. C., Laba, M. Moving away from the geostatistical lamppost: Why, where, and how does the spatial heterogeneity of soils matter? Ecological Modelling. DOI: 10.1016/j.ecolmodel.2014.03.018.
Franzluebbers, A. J. Soil organic matter stratification ratio as an indicator of soil quality. Soil and Tillage Research, 2002, 66, 95-106.
Zougmoré, R., Zida, Z., Kambou, N. F. Role of nutrient amendments in the success of half-moon soil and water conservation practice in semiarid Burkina Faso. Soil and Tillage Research. 2003, 71, 143-149.
Marchetti, A., Piccini, C., Francaviglia, R., Mabit, L. Spatial Distribution of Soil Organic Matter Using Geostatistics: A Key Indicator to Assess Soil Degradation Status in Central Italy. Pedosphere. 2012, 22, 230-242.
de la Rosa, D., Anaya-Romero, M., Diaz-Pereira, E., Heredia, N., Shahbazi, F. Soil-specific agro-ecological strategies for sustainable land use–A case study by using MicroLEIS DSS in Sevilla Province (Spain). Land Use Policy. 2009, 26, 1055-1065.
Kölbl, A. et al. Spatial distribution of SOM parameters during paddy soil evolution, EGU General Assembly Conference Abstracts. 2010, pp. 2770.
Li, Q. Q. et al. Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach. CATENA. 2013, 104, 210-218.
Lionel, M., Claude, B. Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada, as estimated from geostatistical tools. Earth Surface Processes and Landforms. 2010, 35, 278-283.
Chen, F. R., Qin, F., Li, X., Peng, G. X. Inversion for spatial distribution of soil organic matter content based on multivariatev geostatistics. Transactions of the Chinese Society of Agricultural Engineering. 2012, 28 (20), 188-194. (in Chinese with English abstract)
Zhang, S. W., Huang, Y. F., Shen, C. Y., Ye, H. C., Du, Y. C. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information. Geoderma. 2012, 171, 35-43.
Dai, F. Q., Zhou, Q. G., Lv, Z. Q., Wang, X. M., Liu, G. C. Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators. 2014, 45, 184-194.
Guo, P. T.; Li, M. F.; Luo, W.; Tang, Q. F.; Liu, Z. W.; Lin, Z. M. Digital mapping of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Geoderma. 2015, 237–238, 49–59.
Mirzaee, S.; Ghorbani-Dashtaki, S.; Mohammadi, J.; Asadi, H.; Asadzadeh, F. Spatial variability of soil organic matter using remote sensing data. Catena. 2016, 145, 118–127.
Shi, S. Q., Cao, Q. W., Yao, Y. M., Tang, H. J., Yang, P., Wu, W. B., Xu, H. Z., Liu, J., Li, Z. G. Influence of climate and socio-economic factors on the spatio-temporal variability of soil organic matter: a case study of central Heilongjiang Province, China. Journal of Integrative Agriculture. 2014, 13, 1486-1500.
Wang, H. J., Shi X. Z., Yu D. S., Weindorf, D. C., Huang, B., Sun, W. X., Ritsema, C. J., Milne, E. Factors determining soil nutrient distribution in a small-scaled watershed in the purple soil region of Sichuan Province, China. Soil and Tillage Research. 2009, 105, 300-306.
Umali, B. P., Oliver, D. P., Forrester, S., Chittleborough, D. J., Hutson, J. L., Kookana, R. S., Ostendorf, B. The effect of terrain and management on the spatial variability of soil properties in an apple orchard. CATENA. 2012, 93, 38-48.
Romstad, B., Etzelmüller, B. Mean-curvature watersheds: A simple method for segmentation of a digital elevation model into terrain units. Geomorphology. 2012, 139–140, 293-302.
Wei, J. L., Wang, G. B., Ling, Z. Y. The extraction and analysis of landform characters based on high-resolution DEM. Geomatics & Spatial Information Technology. 2012, 35 (1), 33-36. (in Chinese English abstract).
Tian, R. Y., Wang Y. K., Fu B., liu Y. DEM-based topographic unit diversity index and its algorithm. Progress in Geography. 2013, 32 (1), 121-129. (in Chinese with English abstract).
CNIC (Computer Network Information Center), 2012. International Scientific & Technical Data Mirror Site.
MOA (Ministry of Agriculture of the People's Republic of China). 2006, NY/T1121.6-2006, Soil testing, part 6: method for determination of soil organic matter.
Moore, I. D., Grayson, R. B., Ladson, A. R. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrological Processes. 1991, 5, 3-30.
Thompson, J. A., Bell, J. C., Butler, C. A. Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma. 2001, 100, 67-89.
Weiss, A. D. Topographic position and landforms analysis, Poster presentation, ESRI User Conference, San Diego, CA. 2001, pp. 200-200.
Anbalagan, R. Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering Geology. 1992, 32, 269-277.
Quinn P., Beven K. J., Lamb R. The Ln (α/tanβ) Index: How to calculate it and how to use it within the TOPMODEL framework. Hydrological Processes. 1995, 9 (2): 161-182.
Palmer, W. C., 1965. Meteorological drought. US Department of Commerce, Weather Bureau Washington, DC, USA.
Sen Gupta, A., Jain, S., Kim, J.-S. Past climate, future perspective: An exploratory analysis using climate proxies and drought risk assessment to inform water resources management and policy in Maine, USA. Journal of Environmental Management. 2011, 92, 941-947.
Anselin L., Syabri I., Kho Y. GeoDa: an introduction to spatial data analysis. Geographical analysis. 2006, 38 (1): 5-22.
Zhang, S. M. Wang Z. G., Zhang B., Song K. S., Liu D. W., Li F., Ren C. Y., Huang J., Zhang H. L. Prediction of spatial distribution of soil nutrients using terrain attributes and remote sensing data. Transactions of the Chinese Society of Agricultural Engineering. 2010, 26 (5), 188-194. (in Chinese with English abstract).
Wen, W., Wang, YF., Yang, L. Mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess Plateau of China: a comparative study based on three kriging methods and a soil land inference model (SoLIM). ENVIRONMENTAL EARTH SCIENCES. 2015, 73 (1), 239-251.
Lydia M. C., Augustine M.; Obed L. Application of Ordinary Kriging in Mapping Soil Organic Carbon in Zambia. Pedosphere. 2017, 27 (2): 338–343.
Bameri, A., F. Khormali, F. Kiani & AA. Dehghani. Spatial variability of soil organic carbon in different hill slope positions in Toshan area, Golestan Province, Iran: geostatistical approaches. Journal of Mountain Science. 2015, 12 (6): 1422–1433.
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