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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.
Based on near-infrared spectroscopy data obtained from field walking equipment, a correction model for predicting soil carbon content using near-infrared spectroscopy data was established by least square regression (PLSR). Compared with the model established by using original spectral data, The calibration model established by the spectral data processed by the ratio or normalized difference can improve the prediction accuracy.The reason of the precision improvement may be that the spectral data after the band arithmetic combination processing can reduce the risk of over-allocation in the model establishment process, Can contain more components and information.The results show that the use of partial least-squares regression method can effectively establish the field near-infrared spectroscopy and soil carbon content between the correction model; the same time, the ratio or normalized difference Band arithmetic combination method to deal with near-infrared spectroscopy data can further improve the prediction accuracy of the model.Therefore, it is feasible to use near-infrared spectroscopy data from walking equipment to quickly determine the soil carbon content in the field.