Abstract: The hyperspectral characteristics of different particle contents of soil in the typical alluvial plain of the lower Yellow River were investigated in order to provide technical support for rapid monitoring and evaluation of soil texture. Seven spectral transformation forms were selected, including the original spectrum and its reciprocal, logarithm, standard orthogonal transformation, multivariate scattering change, the first derivative and the second derivative. First, principal component analysis was applied to reduce dimension. Then, predictive models of the contents of clay, silt and sand were established with support vector machines. Three accuracy indices were selected including determination coefficient, mean absolute error and root mean squared error. The results showed that the logarithm of the original spectrum was the best spectral transformation form due to the best prediction with the R² ≥ 0.6853, the MAE ≤ 0.1193 and the RMSE ≤ 0.1683. The variation range of clay content was relatively concentrated, showing the best prediction by the R² of 0.8127, MAE of 0.0820 and RMSE of 0.1248. The soil spectrum was reduced dimension with principal component by selecting the best spectral transformation. Whereafter, the support vector machine modeling was used to predict the contents of clay, silt and sand in soil, which was realized a simple and fast hyperspectral estimation on soil texture.