Abstract: Atmospheric boundary layer is a key parameter for boundary layer studies, including meteorology, air quality and climate. The atmospheric boundary layer height estimates are inferred from local radiosonde measurements or remote sensing observations from instruments like laser radar, wind profiling radar or sodar. Methods used to estimate atmospheric boundary layer height from radiosonde profiles are also used with atmospheric temperature and humidity profiles retrieved by microwave radiometers. An alternative approach to estimate atmospheric boundary layer height from microwave radiometer data is proposed based on microwave brightness temperatures, instead of retrieved profiles. Using the ground-based microwave radiometer and laser radar atmospheric boundary layer height obtained in 2013 at Xianghe Station, algorithms for retrieving atmospheric boundary layer height from 14-channel microwave brightness temperatures are developed based on the nonlinear neural network and multiple linear regression methods. The atmospheric boundary layer height is derived from laser radar backscattering data using the algorithm that retrieves the most significant gradients in profiles using gradient method. Root mean square errors (RMSEs) and correlation coefficient with two kinds of method are obtained to analyze which method is better through comparison. Retrieval results with the neural network method are compared in different periods of time and weather conditions. It shows that neural network algorithm is better than the multiple linear regression algorithm because results are more consistent with the observation. The correlation coefficient between the lidar-detected and neural network algorithm retrieved boundary layer height is 0.83, which is about 26% higher than the multiple linear regression algorithm retrieved result. Also, RMSEs of the neural network algorithm retrieved values (268.8 m) are less than the multiple linear regression algorithm retrieved values (365.1 m). For different time periods and weather conditions, retrievals in spring are best of four seasons, retrievals in the clear sky are better than those in the cloudy sky. But RMSEs in the cloud sky are less than those in the clear sky. Overall, correlation coefficients in four seasons are close to 0.80. It suggests that in order to improve the retrieval precision, specific retrievals under different conditions (such as different seasons and different skies) should be carried out.