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Simulating Spatial Variation of Soil Carbon Content in the Yellow River Delta: Comparative Analysis of Two Artificial Neural Network Models

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Abstract

A reasonable predicting model for spatial variation of soil carbon would be a useful tool in monitoring and restoration of salt marshes. In this study, radial basis function neural networks model (RBFNN) and back propagation neural networks model (BPNN) were built to predict total carbon (TC), total organic carbon (TOC) and dissolved organic carbon (DOC) contents in salt marsh of the Yellow River Delta. Both models contained thirteen input parameters, i.e., nine topographic factors selected from ASTER GDEM Version2 and Geographical Information System (GIS), one vegetation index – MODIS 16-day composite Enhanced Vegetation Index (EVI), and three soil physicochemical properties. For prediction of TC, the MAE, MSE and RMSE values of RBFNN were smaller than those of BPNN by 61.87%, 81.36% and 56.82%; for TOC, the MAE, MSE and RMSE values of RBFNN were smaller than those of BPNN by 37.13%, 58.06% and 35.23%; both models had no significant difference in accuracy for DOC prediction, but the MAE, MSE and RMSE values of RBFNN were smaller. All ME values of RBFNN rather than BPNN were closer to zero. RBFNN integrating environmental factors had a higher accuracy than BPNN in predicting soil carbon content at a relatively small regional scale.

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Abbreviations

ANN :

artificial neural network

RBFNN :

radial basis function neural networks model

BPNN :

back propagation neural networks model

TC :

total carbon

TOC :

total organic carbon

DOC :

dissolved organic carbon

EC :

electric conductivity

YRD :

Yellow River delta

DEM :

Digital Elevation Model

TWI :

topographic wetness index

RA :

relief amplitude

SOS :

slope of slope

SR :

surface roughness

LS :

slope length and steepness factor

PlanC :

plan curvature

ProfC :

profile curvature

GIS :

Geographical Information System

EVI :

Enhanced Vegetation Index

MAE :

mean absolute error

MSE :

mean squared Error

RMSE :

root mean squared error

ME :

mean error

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Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (51609005), the National Key R&D Program of China (2017YFC0505903) and the Fundamental Research Fund for the Central Universities (2015ZCQ-BH-01). The authors gratefully acknowledge Profs. Baoshan Cui and Junhong Bai, and lab members Lidi Zheng, Jingxiao Chen, Zhuoqun Wei, Yueyan Pan and the anonymous reviewers for their great helps and valuable suggestions.

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Authors and Affiliations

  1. School of Nature Conservation, Beijing Forestry University, Beijing, 100083, People’s Republic of China

    Chen Wang, Yuan Cui, Ziwen Ma, Yutong Guo, Qian Wang, Yujiao Xiu, Rong Xiao & Mingxiang Zhang

  2. College of Environment and Resources, Fuzhou University, Fuzhou, 350116, People’s Republic of China

    Rong Xiao

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  1. Chen Wang
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  2. Yuan Cui
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Correspondence to Rong Xiao or Mingxiang Zhang.

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Wang, C., Cui, Y., Ma, Z. et al. Simulating Spatial Variation of Soil Carbon Content in the Yellow River Delta: Comparative Analysis of Two Artificial Neural Network Models. Wetlands 40, 223–233 (2020). https://doi.org/10.1007/s13157-019-01170-x

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