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Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach

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  • Tao Liu

    (College of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450002, China
    Key laboratory of New Materials and Facilities for Rural Renewable Energy (MOA of China), Henan Agricultural University, Zhengzhou 450002, China)

  • Huan Zhang

    (Key laboratory of New Materials and Facilities for Rural Renewable Energy (MOA of China), Henan Agricultural University, Zhengzhou 450002, China)

  • Tiezhu Shi

    (MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
    School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China)

Abstract

Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km 2 ) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R 2 ), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.

Suggested Citation

  • Tao Liu & Huan Zhang & Tiezhu Shi, 2020. "Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach," Sustainability, MDPI, vol. 12(22), pages 1-12, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9330-:d:442705
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    References listed on IDEAS

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    1. Antoine Stevens & Marco Nocita & Gergely Tóth & Luca Montanarella & Bas van Wesemael, 2013. "Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-13, June.
    2. Eric A. Davidson & Ivan A. Janssens, 2006. "Temperature sensitivity of soil carbon decomposition and feedbacks to climate change," Nature, Nature, vol. 440(7081), pages 165-173, March.
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