Abstract: We construct the soil moisture retrieval model of GNSS satellite reflection signal based on machine learning algorithm with the use of BP neural network and support vector machine,and compared with the linear regression statistical model and the measured data.The results show that the error between soil moisture prediction of retrieval model based on the regression algorithm and reference value of soil moisture is small,the determination coefficient are 0.928 3 and 0.913 1,respectively,the root mean square error are 0.026 6 and 0.032 0,and the determination coefficient of the linear regression statistic model are 0.553 8 and 0.859 2,respectively,the root mean square error are 0.093 9 and 0.041 6.Those demonstrates that the quantitative estimates of soil moisture by using regression algorithm is better than the linear regression model,and the soil moisture retrieval model based on support vector regression is superior to the soil moisture retrieval model based on BP neural network algorithm,which proves the reliability of the method and provides a new method for real-time retrieval of soil moisture.

Key words: BP neural network algorithm, support vector regression machine, signal to noise ratio, soil moisture