土壤水分对近红外光谱实时检测土壤全氮的影响研究

利用近红外光谱技术实时预测土壤全氮含量是精细农业的研究热点之一，但是由于土壤水分在近红外波段的吸收系数较高，影响了土壤全氮含量的实时预测精度。使用布鲁克MATRIX_I傅里叶近红外光谱分析仪对不同土壤水分的土壤样本进行了近红外光谱扫描，定性和定量的分析了土壤水分对近红外光谱的影响，并提出了一种消除土壤水分对土壤全氮含量预测影响的方法。近红外光谱扫描结果显示在同一全氮含量水平下，随着土壤水分含量的增加，光谱吸光度呈逐渐上升的趋势，且变化趋势为非线性。通过对1 450和1 940 nm两个水分吸收波段的差分处理，设计了水分吸收指数MAI（moisture absorbance index），再对土壤按照水分含量梯度进行分类，提出了相应的修正系数。修正后的6个土壤全氮特征波段处(940，1 050，1 100，1 200，1 300和1 550 nm)的土壤吸光度值作为建模自变量，使用BP神经网络建立了土壤全氮预测模型，模型的R_C，R_V，RMSEC，RMSEP和RPD分别达到了0.86，0.81，0.06，0.05和2.75;与原始吸光度所建模型相比较模型精度得到了显著提高。实验结果表明本方法可以有效地消除土壤水分对近红外光谱检测土壤全氮含量预测的影响，为土壤全氮含量实时预测提供了理论和技术支持。

Effect of Soil Moisture on Prediction of Soil Total Nitrogen Using NIR Spectroscopy

As one of the most important components of soil nutrient, it is necessary to obtain the soil total nitrogen（STN）content in precision agriculture. It is a feasible method to predict soil total nitrogen content based on NIRS. However, the effect of soil moisture content (SMC) on the prediction of STN is very serious. In the present research, the effect of SMC was discussed from qualitative analysis and quantitative analysis by the Fourier spectrum analyzer MATRIX_I. Firstly, sixty soil samples with different STN and SMC were scanned by the MATRIX_I. It was found that the reflectance of soil samples in near infrared region decreased with the increase in SMC. Subsequently, Moisture absorbance index (MAI) was proposed by the diffuse of absorbance at the wavelengths of 1 450 and 1 940 nm to classify soil properties and then correction factor was present. Finally, the STN forecasting model with BP NN method was established by the revised absorbance data at the six wavelengths of 940, 1 050, 1 100, 1 200, 1 300 and 1 550 nm. The model was evaluated by correlation coefficient of R_C, correlation coefficient of R_V, root mean square error of calibration (RMSEC), root mean square error of validation (RMSEP) and residual prediction deviation (RPD). Compared with the model obtained from original spectral data, both the accuracy and the stability were improved. The new model was with R_C of 0.86, R_V of 0.81, RMSEC of 0.06, RMSEP of 0.05, and RPD of 2.75. With the first derivative of the revised absorbance, the RPD became 2.90. The experiments indicated that the method could eliminate the effect of SMC on the prediction of STN efficiently.