以田间行走式设备获取的近红外光谱数据为基础, 利用最小二乘回归法(PLSR)建立了应用近红外光谱数据预测土壤碳含量的校正模型, 与利用原始光谱数据建立的模型相比, 应用经比值或归一化差值处理的光谱数据建立的校正模型可以提高预测精度.精度提高的原因可能是光谱数据经过波段算术组合处理后, 能降低模型建立过程中产生过配的风险, 使模型能包括更多的成分和信息.研究结果表明, 利用偏最小二乘回归法, 可以有效地建立田间近红外光谱与土壤碳含量之间的校正模型；同时, 应用比值或归一化差值这些波段算术组合方法来处理近红外光谱数据, 可以进一步提高模型的预测精度.因此, 应用行走式设备获取的近红外光谱数据来快速测定田间土壤中碳的含量是可行的.

Partial least squares regression (PLSR) was employed to build predicting model of the content of soil carbon with on-the-go near-infrared reflectance spectroscopy (NIRS) measurements. The model based on band ratio or normalized difference of NIRS data can improve the prediction precision than the model with the original NIRS data. The reasons might be that the process of band arithmetic combination could reduce the risk of overfitting and it made the model include more useful components and information. The results show that the effective calibration model between field NIRS and the content of soil carbon can be set up by PLSR, and predicting precision can be improved while band arithmetic combination of ratio or normalized difference is performed on the NIRS data before modeling. Thus, it is feasible to estimate the content of soil carbon quickly in the field by on-the-go NIRS measurement.