To explore the feasibility of measuring total and available soil nitrogen by using near-infrared spectroscopy in the field, the calibration models were respectively established on the basis of soil spectrum signals, as well as partial least squares method (PLS) and principal components analysis (PCA). The results showed that the models of total and available soil nitrogen established by using PLS approach were more accurate. To further improve the precision of the models, five different pretreatment methods were adopted to process the spectrum signals, including multiplicative scatter correction, standard normalization, baseline correction, convolution, smoothing, and wavelet transformation. The highest precision model was derived from wavelet denoising combined with PLS. The correlation coefficient (R) and the root mean square error (RMSE) of the calibration model for total soil nitrogen were 0.838 5 and 0.153 1, respectively. The correlation coefficient and the root mean square error of the corresponding verification model were 0.754 9 and 0.184 2, respectively. The relationship models between the predicted and measured values of total soil nitrogen for the calibration data set and the verification data set were: y=0.685 8x+0.198 0 and y=0.621 4x+0.237 9, where x is the measured total soil nitrogen value, y is the predicted value of total soil nitrogen. In the calibration model of available soil nitrogen, R and RMSE were 0.866 5 and 0.007 7, respectively, and the corresponding values for the verification model were 0.796 1 and 0.009 4, respectively. The relationship models between the predicted and measured values of available soil nitrogen for the calibration data set and the verification data set were y=0.749 8x+0.019 4 and y=0.700 7x+0.023 3，where x is the measured available soil nitrogen value, y is the predicted value of available soil nitrogen. Therefore, it is feasible to apply NIR spectroscopy technology in quantitative determination of total and available soil nitrogen, and the wavelet transformation preprocessing method in combination with PLS can effectively improve the accuracy of the prediction models.