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Investigating Spatial and Vertical Patterns of Wetland Soil Organic Carbon Concentrations in China’s Western Songnen Plain by Comparing Different Algorithms

Author

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  • Yongxing Ren

    (College of Earth Science, Jilin University, Changchun 130100, China
    Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China)

  • Xiaoyan Li

    (College of Earth Science, Jilin University, Changchun 130100, China)

  • Dehua Mao

    (Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China)

  • Zongming Wang

    (Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China
    National Earth System Science Data Center, Beijing 100101, China)

  • Mingming Jia

    (Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China)

  • Lin Chen

    (Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China)

Abstract

Investigating the spatial and vertical patterns of wetland soil organic carbon concentration (SOCc) is important for understanding the regional carbon cycle and managing the wetland ecosystem. By integrating 160 wetland soil profile samples and environmental variables from climatic, topographical, and remote sensing data, we spatially predicted the SOCc of wetlands in China’s Western Songnen Plain by comparing four algorithms: random forest (RF), support vector machine (SVM) for regression, inverse distance weighted (IDW), and ordinary kriging (OK). The predicted results of the SOCc from the different algorithms were validated against independent testing samples according to the mean error, root mean squared error, and correlation coefficient. The results show that the measured SOCc values at depths of 0–30, 30–60, and 60–100 cm were 15.28, 7.57, and 5.22 g·kg −1 , respectively. An assessment revealed that the RF algorithm was most accurate for predicting SOCc; its correlation coefficients at the different depths were 0.82, 0.59, and 0.51, respectively. The attribute importance from the RF indicates that environmental variables have various effects on the SOCc at different depths. The land surface temperature and land surface water index had a stronger influence on the spatial distribution of SOCc at the depths of 0–30 and 30–60 cm, whereas topographic factors, such as altitude, had a stronger influence within 60–100 cm. The predicted SOCc of each vertical depth increased gradually from south to north in the study area. This research provides an important case study for predicting SOCc, including selecting factors and algorithms, and helps with understanding the carbon cycles of regional wetlands.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:932-:d:313542
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    References listed on IDEAS

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    1. 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.
    2. Weidong Man & Hao Yu & Lin Li & Mingyue Liu & Dehua Mao & Chunying Ren & Zongming Wang & Mingming Jia & Zhenghong Miao & Chunyan Lu & Huiying Li, 2017. "Spatial Expansion and Soil Organic Carbon Storage Changes of Croplands in the Sanjiang Plain, China," Sustainability, MDPI, vol. 9(4), pages 1-17, April.
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