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Aiming to promptly obtain soil moisture content (SMC) in the root zone of field crops for precise irrigation, hyperspectral technology was utilized. Over a 2year period spanning from 2019 to 2020, during the winter wheat jointing stage, SMC data at varying soil depths and hyperspectral data were collected. Three categories of vegetation indices were created, comprising ‘trilateral’ spectral parameters related to blue, yellow, and rededge areas, any two-band vegetation indices with the highest correlation to winter wheat root zone SMC, and empirical vegetation indices showing good correlation with crop parameters from previous studies. The vegetation indices exhibited the highest correlation with SMC at different soil depths were selected. Subsequently, random forest (RF), back propagation neural network (BPNN), and extreme learning machine (ELM) were employed to construct SMC estimation models, using the selected vegetation indices as model inputs. The results revealed that a majority of the ‘trilateral’ spectral parameters spectral indices, any two-band vegetation indices, and empirical vegetation indices displayed stronger correlations with SMC in the 0~20cm soil layer in comparison with the 20~40cm and 40~60cm layers. The two-band combinations in the 0~20cm layer exhibited the highest correlations with SMC, all surpassing 0.8. Among which, RI showed the highest correlation with SMC at 0.851, utilizing a wavelength combination of 675nm and 695nm. The RF model emerged as the most effective modeling method for SMC, with the highest accuracy observed in the 0~20cm soil layer. The coefficient of determination (R2) for the validation set of the estimation model in the 0~20cm layer reached 0.909, and the root mean square error (RMSE) was 0.008, while the mean relative error (MRE) was 3.949%. The outcomes can serve as a foundation for hyperspectral monitoring of winter wheat root zone SMC and provide valuable insights for the rapid assessment of crop growth under water stress.