Threshold optimization for forest soil organic matter prediction model: a case study of Guangdong Province of southern China

Zhan Siying; Liu Pengju; Li Haikui; Zhang Cong; Liu Qi

doi:10.12171/j.1000-1522.20240314

Journal of Beijing Forestry University > 2025 > 47(2): 32-39. > DOI: 10.12171/j.1000-1522.20240314

Zhan Siying, Liu Pengju, Li Haikui, Zhang Cong, Liu Qi. Threshold optimization for forest soil organic matter prediction model: a case study of Guangdong Province of southern China[J]. Journal of Beijing Forestry University, 2025, 47(2): 32-39. DOI: 10.12171/j.1000-1522.20240314

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Threshold optimization for forest soil organic matter prediction model: a case study of Guangdong Province of southern China

Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Beijing 100091, China

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Received Date: September 19, 2024
Revised Date: November 24, 2024
Available Online: February 16, 2025

Graphical Abstract

Abstract

Abstract

Objective
This paper takes Guangdong Province of southern China as an example, and explores the applicability of optimized geographical similarity model in the prediction of forest soil organic matter at provincial scale.
Method
Based on the data of 1 175 sample points from soil fertility testing system in Guangdong Province, we selected soil factors, stand factors and climate factors as environmental covariates to improve the “individual point representativeness digital soil mapping (iPSM)”. This article evaluates the applicability of the model in predicting forest soil organic matter at the provincial level using three indicators: coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE), and compares its predictive performance with that of random forest prediction model.
Result
(1) The prediction accuracy of optimized iPSM was better than that of random forest prediction model, and the coefficient of determination of model can reach 0.7419. (2) The total nitrogen content and available potassium content in soil factors were the most influential environmental covariates in prediction model of forest soil organic matter in Guangdong Province.
Conclusion
Compared with iPSM method, the optimized iPSM has improved the accuracy of forest soil organic matter prediction in Guangdong Province. The model has good applicability at the provincial level and can provide a new method for predicting soil organic matter at the provincial level.
- soil organic matter,
- digital soil mapping,
- environmental variable,
- geographical similarity; model optimization

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References

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Threshold optimization for forest soil organic matter prediction model: a case study of Guangdong Province of southern China