土壤有机碳(SOC)是全球碳循环的重要组成部分,在生态系统健康和碳平衡中发挥着重要作用。在这项研究中,我们重点评估了山东省基于土地利用类型的地表SOC含量,探讨了其空间分布格局及影响因素。机器学习方法,包括随机森林(RF),极端梯度提升(XGBoost),和支持向量机(SVM)被用来估计山东省的表面SOC含量使用不同的数据源,如样本数据,遥感数据,社会经济数据,土壤质地数据,地形数据,和气象数据。结果表明,山东省SOC含量为8.78g/kg,在不同地区表现出显著的差异。比较模型误差和相关系数,XGBoost模型显示出最高的预测精度,确定系数(R²)为0.7548,均方根误差(RMSE)为7.6792,相对百分比差异(RPD)为1.1311。高程和粘土在澄清山东省地表SOC含量方面表现出最高的解释力,贡献21.74%和13.47%,分别。空间分布分析表明,与耕地覆盖的平原和沿海地区相比,森林覆盖的山区的SOC含量更高。总之,这些发现为土地利用规划和SOC保护提供了宝贵的科学见解。
Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use types, and explored its spatial distribution pattern and influencing factors. Machine learning methods including random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were employed to estimate the surface SOC content in Shandong Province using diverse data sources like sample data, remote sensing data, socio-economic data, soil texture data, topographic data, and meteorological data. The results revealed that the SOC content in Shandong Province was 8.78 g/kg, exhibiting significant variation across different regions. Comparing the model error and correlation coefficient, the XGBoost model showed the highest prediction accuracy, with a coefficient of determination (R²) of 0.7548, root mean square error (RMSE) of 7.6792, and relative percentage difference (RPD) of 1.1311. Elevation and Clay exhibited the highest explanatory power in clarifying the surface SOC content in Shandong Province, contributing 21.74% and 13.47%, respectively. The spatial distribution analysis revealed that SOC content was higher in forest-covered mountainous regions compared to cropland-covered plains and coastal areas. In conclusion, these findings offer valuable scientific insights for land use planning and SOC conservation.