Hyperspectral remote sensing technology can realize the rapid, real-time, and non-destructive monitoring of soil nutrient changes, which is of great significance to promote the development of precision agriculture. In this paper, 225 soil samples were taken as the research object to study the influence of different water treatment on soil organic carbon content, and the relationship between soil organic carbon content and spectral reflectance. After spectral preprocessing, the hyperspectral monitoring models of SOC content were constructed by partial least squares regression(PLSR) with five different sample allocation ratios of calibration to validation sets. The results showed that the effects of drought stress on SOC content were different in different growth stages of winter wheat. Results of correlation analysis showed that the differential transformation can refine the spectral characteristics and improve the correlation between SOC content and spectral reflectance. Results of model construction showed that the models constructed by second-order differential transformation were not effective, but the RPD values of the models were constructed by simple mathematical transformation(T0-T5) and first-order differential transformation(T6-T11) can reach more than 1.4. The simple mathematical transformation(T0-T2, T4-T5) and the first-order differential transformation(T6-T10) resulted in the highest RPD in mode 5 and mode 2, respectively. Among all the models, the model of T7 in mode 2 reach the highest accuracy with a RPD value of 1.9861. Therefore, it is necessary to consider the data preprocessing algorithm and allocation ratio in the construction of SOC hyperspectral monitoring model.