Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China

Authors

  • Zhongqi ZHANG,

    1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
    2. Graduate University of the Chinese Academy of Sciences, Beijing 100039, China
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  • Dongsheng YU,

    1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
    2. Graduate University of the Chinese Academy of Sciences, Beijing 100039, China
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  • Xuezheng SHI,

    1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
    2. Graduate University of the Chinese Academy of Sciences, Beijing 100039, China
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  • Eric WARNER,

    1. Office of Remote Sensing and Information Resources, Penn State Institutes of the Environment, Pennsylvania State University, University Park, Pennsylvania 16802, USA
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  • Hongyan REN,

    1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
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  • Weixia SUN,

    1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
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  • Manzhi TAN,

    1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
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  • Hongjie WANG

    1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008
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D. S. YU, State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China. Email: dshyu@issas.ac.cn

Abstract

Predicting soil organic carbon (SOC) content distribution accurately from limited soil samples has received a great deal of attention recently in an effort to support soil fertility mapping and to improve our understanding of carbon sequestration variability. Kriging methods combined with auxiliary variables are frequently used at present. However, studies using categorical information, such as soil type and land use, which are closely related to local trends in SOC spatial variation, as auxiliary variables are seldom conducted. In the present investigation, a total of 254 surficial soil samples were collected in the study area, Yujiang county in the hilly red soil region of South China, and a comparison of performance of four kriging approaches was conducted, ordinary kriging (OK), kriging combined with soil-type information (KST), land use (KLU) and combined land use–soil type information (KLUST). Results of the assessment were based on 85 validation samples. The results indicate that the best correlation between the measured and predicted values for validation location was obtained with KLUST (r = 0.854), whereas the lowest was obtained using OK (r = 0.383). Furthermore, the root mean square error (RMSE) from KLUST (3.47 g kg−1) is the lowest, whereas the one obtained using OK (6.49 g kg−1) is the highest. The correlation coefficient and RMSE from KST (r = 0.784, RMSE = 4.15 g kg−1) and KLU (r = 0.795, RMSE = 3.95 g kg−1) are the second and third most correlated, respectively. Comparing the SOC distribution maps generated by the four prediction approaches, the KLUST rendering best reflects the local change associated with soil types and land uses, whereas the map from the OK is the least representative. The results demonstrate that soil type and land use have an important impact on SOC spatial distribution, and KLUST, which reduces their influence as a local trend, is an efficient and practical prediction approach for the hilly red soil region of South China.

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