建模方法是影响可见近红外光谱定量结果的主要因素之一。在470~1000 nm波段的12个土壤剖面对48个剖面样经过风干、研磨、过筛后进行光谱采集。经一阶微分变换及Savizky-Golay平滑处理后，分别应用主成分回归(PCR)、偏最小二乘回归(PLSR)和反向传播神经网络(BPNN)3种方法建立土壤全氮(TN)的定量模型。 PCR与PLSR两线性模型的决定系数(R2)分别为0.74和0.8，其剩余预测偏差(RPD)分别为2.23和2.22，但两模型仅能用于TN的粗略估计。由PCR提供主成分数，PLSR提供潜变量(LV)数分别作为BPNN的输入所构建的两个非线性模型均明显优于线性模型PCR和PLSR。其中以4个LV作为输入的BPNN-LV模型预测性能最优，R2 以及RPD分别达到0.9和3.11。实验结果表明，提取可见近红外光谱的PLSR LV因子作为BPNN的输入，所建定量模型可用于土壤氮纵向时空分布的快速准确预测。

The selection of modeling method is one of the main factors influencing the quantitative accuracy with visible-near infrared (Vis-NIR) spectroscopy. We compare the performance of three calibrations methods, i.e., principal component regression (PCR), partial least squares regression (PLSR), and back propagation neural network (BPNN) based on Vis-NIR reflectance spectra of soil total nitrogen (TN) quantitative forecast results. Covered in the 470~1000 nm wavelength range, spectroscopy of 48 soil samples selected from 12 profiles are air-dried, screened and mushed, then processed by the first order derivative and Savizky-Golay smoothing methods. Leave-one-out cross validation is also adopted to determine the optimal factor numbers. The results indicate that PCR and PLSR linear models are able to meet general prediction and with little difference, where coefficients of determination (R2) are 0.74 and 0.8, respectively, and residual predictive deviation (RPDs) are 2.23 and 2.22. The two nonlinear models built by BPNN in combination with PCR and PLSR, respectively, are superior to the linear models of PCR and PLSR in the precision of prediction. BPNN, principal components (PCs) whose input is the PCs resulted from the PCR, while the BPNN latent variables (LVs) whose input is the first 4 LV results obtained from PLSR has the best performance (R2=0.9, RPD is3.11). It is recommended to adopt BPNN-LV model to rapidly predict the vertical spatial and temporal distribution of TN with Vis-NIR spectroscopy.