Soil water content (θ) is an important factor for the crop growth and crop production. The objectives of this study were to (i) test various regression models for estimating θ based on spectral feature parameters, and (ii) compare the performance of the proposed models by using artificial neural networks (ANN) and spectral feature parameters. The θ data of sand and loam and concurrent spectral parameters were acquired at the laboratory experiment in 2014. The results showed that: (1) the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm estimate θ had the significant, when sand bulk density was 1.40 g·cm－3; the maximum reflectance with blue edge and the sum reflectance within 900～970 nm had the best correlation (R2>0.70) when sand bulk density was 1.50 g·cm－3; while soil bulk density was 1.60 g·cm－3, the sum reflectance within 780～970 nm and normalized absorption depth in 560～760 nm reached a significant (R2>0.90); when soil bulk density was 1.70 g·cm－3, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had the best correlation estimate θ (R2>0.88). 2) When the soil type was loam, the maximum reflectance with 900～970 nm and the sum reflectance within 900～970 nm had a best correlation estimate θ. The spectral feature parameters the sum reflectance within blue edge (R2=0.26 and RMSE=0.09 m3·m－3) and 780～970 nm absorption depth (R2=0.32 and RMSE=0.10 m3·m－3) were best correlated with θ in the sand. The θ model based on maximum reflectance with 900～970 nm (R2=0.92 and RMSE=0.05 m3·m－3) and the sum reflectance within 900～970 nm had a high correlation (R2=0.92 and RMSE=0.04 m3·m－3) in the loam. The BP-ANN model presented a better estimation accuracy of θ (R2=0.87 and RMSE=0.05 m3·m－3) in two soils. Thus, the ANN model has great potential for estimating θ. Thus, the BP-ANN model has great potential for θ estimation.