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Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

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  • Ying-Qiang Song

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Lian-An Yang

    (College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China)

  • Bo Li

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, Guangzhou 510642, China)

  • Yue-Ming Hu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Key Laboratory for Land Use and Consolidation, Guangzhou 510642, China)

  • An-Le Wang

    (Lantian County Agricultural Technology Popularization Center, Xi’an 710500, China)

  • Wu Zhou

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Xue-Sen Cui

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Yi-Lun Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, Guangzhou 510642, China)

Abstract

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study was conducted in a regional scale study area in Shaanxi Province, China. A total of 472 topsoil (0–20 cm) samples were collected. A total of 14 auxiliary variables (predictors) were obtained from remote sensing data and environmental factors. The proposed method was compared with the ability of traditional geostatistical methods such as simple kriging (SK) and ordinary kriging (OK), in addition to hybrid geostatistical methods such as regression-ordinary kriging (ROK) and artificial neural network-ordinary kriging (ANNOK). The results showed that the extreme learning machines (ELM) model used principal components (PCs) as input variables, and performed better than both multiple linear regression (MLR) and artificial neural network (ANN) models. Compared with geostatistical and hybrid geostatistical prediction methods of SOM spatial distribution, the ELMOK model had the highest coefficient of determination (R 2 = 0.671) and ratio of performance to deviation (RPD = 2.05), as well as the lowest root mean square error (RMSE = 1.402 g kg −1 ). In conclusion, the application of remote sensing imagery and environmental factors has a deeper driven significance of a non-linear and multi-dimensional hierarchy relationship for explaining the spatial variability of SOM, tracing local carbon sink and high quality SOM maps. More importantly, it is possibly concluded that the sustainable monitoring of SOM is a significant process through the pixel-based revisit sampling, an analysis of the mapping results of SOM, and methodological integration, which is the primary step in spatial variations and time series. The proposed ELMOK methodology is a promising and effective approach which can play a vital role in predicting the spatial variability of SOM at a regional scale.

Suggested Citation

  • Ying-Qiang Song & Lian-An Yang & Bo Li & Yue-Ming Hu & An-Le Wang & Wu Zhou & Xue-Sen Cui & Yi-Lun Liu, 2017. "Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging," Sustainability, MDPI, vol. 9(5), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:754-:d:97633
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    References listed on IDEAS

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    1. Johannes Lehmann & Markus Kleber, 2015. "The contentious nature of soil organic matter," Nature, Nature, vol. 528(7580), pages 60-68, December.
    2. Mariana Regina Durigan & Maurício Roberto Cherubin & Plínio Barbosa De Camargo & Joice Nunes Ferreira & Erika Berenguer & Toby Alan Gardner & Jos Barlow & Carlos Tadeu dos Santos Dias & Diana Signor &, 2017. "Soil Organic Matter Responses to Anthropogenic Forest Disturbance and Land Use Change in the Eastern Brazilian Amazon," Sustainability, MDPI, vol. 9(3), pages 1-16, March.
    3. Ya-Nan Zhao & Xin-Hua He & Xing-Cheng Huang & Yue-Qiang Zhang & Xiao-Jun Shi, 2016. "Increasing Soil Organic Matter Enhances Inherent Soil Productivity while Offsetting Fertilization Effect under a Rice Cropping System," Sustainability, MDPI, vol. 8(9), pages 1-12, September.
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    Citations

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    Cited by:

    1. Li Wang & Yong Zhou, 2022. "Combining Multitemporal Sentinel-2A Spectral Imaging and Random Forest to Improve the Accuracy of Soil Organic Matter Estimates in the Plough Layer for Cultivated Land," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    2. Fuat Kaya & Ali Keshavarzi & Rosa Francaviglia & Gordana Kaplan & Levent Başayiğit & Mert Dedeoğlu, 2022. "Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus," Agriculture, MDPI, vol. 12(7), pages 1-27, July.
    3. Kingsley JOHN & Isong Abraham Isong & Ndiye Michael Kebonye & Esther Okon Ayito & Prince Chapman Agyeman & Sunday Marcus Afu, 2020. "Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil," Land, MDPI, vol. 9(12), pages 1-20, December.
    4. Ramalingam Kumaraperumal & Sellaperumal Pazhanivelan & Vellingiri Geethalakshmi & Moorthi Nivas Raj & Dhanaraju Muthumanickam & Ragunath Kaliaperumal & Vishnu Shankar & Athira Manikandan Nair & Manoj , 2022. "Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India," Land, MDPI, vol. 11(12), pages 1-26, December.
    5. Huijuan Zhang & Wenkai Liu & Qiuxia Zhang & Xiaodong Huang, 2022. "Three-Dimensional Spatial Distribution and Influential Factors of Soil Total Nitrogen in a Coal Mining Subsidence Area," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    6. Yongxing Ren & Xiaoyan Li & Dehua Mao & Zongming Wang & Mingming Jia & Lin Chen, 2020. "Investigating Spatial and Vertical Patterns of Wetland Soil Organic Carbon Concentrations in China’s Western Songnen Plain by Comparing Different Algorithms," Sustainability, MDPI, vol. 12(3), pages 1-13, January.

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