有限样本下土壤有机碳密度空间分布预测模型对比分析

Comparative Analysis on Models for Predicting the Spatial Distribution of Soil Organic Carbon Density with Limited Samples

[目的]探讨不同机器学习模型在有限样本条件下预测表层土壤有机碳密度(SOCD)空间分布的精度和适用性,为黄土高原流域尺度碳库研究提供参考。[方法]基于延河子流域有限样本,对比多元线性逐步回归(SR)、随机森林(RF)、极端梯度提升(XGB)、支持向量机(SVM)这4种机器学习模型对表层土壤(0—20 cm)SOCD的预测精度和稳定性。[结果](1)在有限样本条件下,4种机器学习模型均可以较好地预测流域尺度SOCD空间分布,其中SVM模型精度最优,其50次预测的RMSE,R^(2),MAE平均值分别为0.74,0.43,0.64;(2)不同土地利用类型的SOCD均值估算结果大小一致且具有显著差异,均为灌木林>林地>草地>耕地,研究区总有机碳储量(0—20 cm)为2.39×10^(6 )t;(3) SOCD空间分布预测因子重要性评价结果表明地形因子、NDVI_(max)、近红外波段地表反射率(B5)以及K-T变化中的Brightness因子对模型预测精度具有显著贡献。[结论]研究表明在有限样本条件下机器学习模型结合相关变量因子可有效应用于黄土高原流域尺度表层SOCD空间分布反演及碳库研究。

[Objective] The aim of this study is to explore the accuracy and applicability of different machine learning models for predicting the spatial distribution of surface soil organic carbon density(SOCD) with limited samples,which can provide a references for the study of watershed scale carbon pool in the Chinese Loess Plateau.[Methods] In this study,we compared the accuracy and stability of the predicted SOCD in topsoil(0—20 cm) by four machine learning models,namely Multiple Linear Stepwise Regression(SR),Random Forest(RF),Extreme Gradient Boosting(XGB) and Support Vector Machine(SVM),based on the limited measured samples in a sub-watershed of Yanhe River in the Chinese Loess Plateau.[Results](1) Under the condition of limited samples,all models successfully and appropriately predicte the spatial distribution of SOCD,among which the SVM model has the best model performance,and the average RMSE,R^(2) and MAE of 50 predictions is 0.74,0.43 and 0.64,respectively.(2) The average SOCD of different land use types are consistent between measured and predicted values but shows significant difference among land use types.SOCD decreases in the order:shrubland>forestland>grassland>cropland.The total organic carbon of cultivated land in the study area is 2.39×10^(6)t(0—20 cm).(3) The evaluation of feature importance shows that terrain factors,NDVI_(max),near-infrared surface reflectance(B5) and Brightness index have significant contributions to the accuracy of predictions.[Conclusion] Under the condition of limited samples,the machine learning model combined with controlling features can be effectively applied to the prediction of the spatial distribution of topsoil SOCD at the watershed scale in the Chinese Loess Plateau.