ABSTRACT

The soil organic carbon (SOC) pool is larger than the biotic and atmospheric carbon pools, and understanding cultivated land SOC is critical for evaluating soil fertility and local agricultural management. Digital soil mapping (DSM) efficiently predicts soil properties by utilizing spatially distributed information. To date, many machine learning techniques have been applied in DSM, but model ensemble methods have rarely been used. In this study, two model ensemble methods, including the Granger-Ramanathan averaging and Bayesian model averaging, and three individual mathematical algorithms, such as the random forest, quantile regression forests, and multivariate adaptive regression splines models, were used to estimate SOC at a local level with limited soil samples. The prediction performances of the five methods were compared using an independent validation dataset composed of field observations of soil samples. The results showed that the prediction accuracy of the ensemble methods was higher than that of the individual methods (ΔR² = 0.02 to 0.21). Compared to the other methods, the Granger-Ramanathan method had a lower root mean square error (RMSE) and mean absolute error and a higher prediction uncertainty. Therefore, the results of this study show the effectiveness of using ensemble methods in mapping SOC with limited soil samples.

摘要

土壤有机碳 (SOC) 库大于生物和大气碳库，了解耕地 SOC 对于评估土壤肥力和当地农业管理至关重要。数字土壤测绘 (DSM) 通过利用空间分布的信息有效地预测土壤特性。迄今为止，许多机器学习技术已经在 DSM 中得到应用，但模型集成方法很少被使用。在这项研究中，两种模型集成方法，包括 Granger-Ramanathan 平均和贝叶斯模型平均，以及三种单独的数学算法，如随机森林、分位数回归森林和多元自适应回归样条模型，用于估计 SOC 在土壤样本有限的地方水平。使用由土壤样品现场观察组成的独立验证数据集比较了五种方法的预测性能。结果表明，集成方法的预测精度高于单个方法（ΔR²  = 0.02 至 0.21)。与其他方法相比，Granger-Ramanathan 方法具有较低的均方根误差 (RMSE) 和平均绝对误差以及较高的预测不确定性。因此，本研究的结果表明，使用集成方法绘制有限土壤样品的 SOC 是有效的。