基于地统计学与机器学习模型的宝清县表层有机碳含量预测及比较研究

doi:10.11720/wtyht.2025.1488

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Abstract

This study aims to accurately predict the organic carbon content in black soils at the county level, thereby supporting county-level agricultural production and carbon peak and neutrality goals. This study examined 427 soil samples obtained from a surface substrate survey of the black soil area in Baoqing County. Employing deterministic interpolation (inverse distance weighting, IDW), geostatistics (ordinary Kriging method, OK), and machine learning (random forest, RF), this study constructed assessment models to predict the organic carbon content in topsoils in Baoqing County and to compare their prediction accuracy and performance. The results show that the IDW, OK, and RF models yielded average organic carbon contents of 27.21×10^-3, 26.33×10^-3, and 32.05×10^-3, respectively. The RF model outperformed the other two models in terms of root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). Specifically, the RF model achieved R² values of 0.73 and 0.53 on training and validation sets, respectively, suggesting significantly higher accuracy. This superior performance demonstrates that the RF model can more fully explore potential patterns in data through the nonlinear interaction of environmental variables. Overall, the RF model, incorporating multiple environmental variables, proved to be the optimal approach for predicting the organic carbon content in topsoils in Baoqing County, demonstrating high prediction accuracy. This study provides valuable theoretical and methodological insights for assessing the spatial variations in soil organic matter relevant to county-level agricultural production and regional differences in carbon peak and neutrality goals within black soil areas.

Key words： black soil area surface substrate survey organic carbon geostatistical method random forest model

Received: 12 December 2024 Published: 23 October 2025

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	Hong-Bo LIU
	Jia-Hui SHI
	Si-Yin WANG
	Jiu-Bo PEI

Cite this article:

Hong-Bo LIU,Jia-Hui SHI,Si-Yin WANG, et al. Prediction and comparison of organic carbon content in topsoils based on geostatistics and machine learning models: A case study of Baoqing County[J]. Geophysical and Geochemical Exploration, 2025, 49(5): 1243-1250.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2025.1488 OR https://www.wutanyuhuatan.com/EN/Y2025/V49/I5/1243

Topography of the study area and location of sampling sites

Data normal distribution plot

Parameters of the semi-variance function model

Soil organic carbon content semi-variance function

Heat map of correlations across environmental variables

Importance analysis of characteristics of environmental variables

Prediction of spatial distribution of soil organic carbon in Baoqing County using inverse distance weighting

Prediction of spatial distribution of soil organic carbon in Baoqing County using ordinary kriging

Prediction of spatial distribution of soil organic carbon in Baoqing County using random forest

Statistical analysis of the results of spatial prediction of organic carbon content

Results of organic carbon interpolation accuracy analysis

Inverse distance weighting method prediction versus actual values

Ordinary kriging prediction versus actual values

Random forest prediction versus actual values

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