标题: The Spatial Distribution and Driving Mechanism of Soil Organic Matter in Hilly Basin Areas Based on Genetic Algorithm Variable Combination Optimization and Shapley Additive Explanations Interpretation

作者: Huang, H (Huang, He); Liu, YL (Liu, Yaolin); Liu, YF (Liu, Yanfang); Tong, ZM (Tong, Zhaomin); Ren, ZQ (Ren, Zhouqiao); Xie, YF (Xie, Yifan)

来源出版物: REMOTE SENSING 卷: 17 期: 7 文献号: 1186 DOI: 10.3390/rs17071186 Published Date: 2025 MAR 27

摘要: Studying the spatial variation patterns and influencing factors of soil organic matter (SOM) in hilly and basin areas is of great significance for guiding agricultural production practices. This study takes Lanxi City as an example and comprehensively considers soil formation factors such as climate, vegetation, and terrain. Based on the genetic algorithm, 47 environmental variables are combined and optimized to construct a random forest (RF) model and an improved version-a random forest model based on genetic algorithm variable combination optimization (RF-GA). At the same time, the SHAP interpretation method is used to quantitatively analyze the spatial distribution characteristics of the SOM content and further identify the main driving factors. Compared with the ordinary Kriging (OK) and random forest (RF) methods, the random forest model based on genetic algorithm variable combination optimization (RF-GA) demonstrates a significantly improved prediction accuracy (R2 = 0.49; RMSE = 3.49 g<middle dot>kg-1), with an MAE = 3.019 and LCCC = 0.67. Among the three models, the R2 of the RF-GA model increases by 87.84% and 56.29%. The model prediction results indicate that the SOM content in the study area ranges from 12.11 to 31.38 g<middle dot>kg-1, showing spatial distribution characteristics of a higher content in mountainous areas and a lower content in plains. A further SHAP analysis shows that terrain, climate, and biological factors are key environmental factors affecting the spatial differentiation of the SOM, with the channel network base level (CNBL), which contributes 20.68% to the model, and DEM, which has a contribution rate of 5.57%, playing particularly significant roles. By regulating moisture, erosion deposition, vegetation distribution, and microclimate conditions, they significantly affect the spatial distribution of the SOM. In summary, the RF-GA and its interpretable prediction model constructed in this study not only effectively reveal the spatial and driving mechanisms of SOM in hilly and basin areas but also provide a solid theoretical basis and practical guidance for accurate mapping, the formulation of sustainable utilization strategies for soil resources, and ensuring national food security.

作者关键词: soil organic matter; genetic algorithm; random forest; SHAP

KeyWords Plus: CARBON STOCKS; TEMPERATURE; CHINA

地址: [Huang, He; Liu, Yaolin; Liu, Yanfang; Tong, Zhaomin; Xie, Yifan] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Ren, Zhouqiao] Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Peoples R China.

通讯作者地址: Liu, YL (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: 2022182050055@whu.edu.cn; yaolin610@yeah.net; yfliu610@yeah.net; tongzm2215@126.com; renzq@zaas.ac.cn; xieyf0815@163.com

影响因子：4.2