基于高光谱的土壤有机碳含量预测研究

摘要

[Objective] To predict soil organic carbon content.[Method] Surface soil was detected by high spectrometer spectrometric and spectral data was treated,through stepwise multiple linear regression (SMLR) and partial least-squares regression (PLSR) method,soil organic carbon content was predicted,and the accuracy of the two models was compared.[Result] The accuracy of PLSR model was higher than SMLR model.[Conclusion] PLSR method is better than SMLR method in forecasting organic carbon.%[目的]对土壤有机碳含量进行预测研究.[方法]利用高光谱仪对表层土壤进行光谱测定并且进行光谱数据的预处理,通过多元线性逐步回归(SMLR)和偏最小二乘回归(PLSR)方法对土壤有机碳含量进行预测,并对2种模型的精度进行比较.[结果]LSR模型的精度高于SMLR模型.[结论]偏最小二乘回归法优于多元逐步回归法,对有机碳的预测具有更好的效果.