Abstract: Continuously operating reference stations (CORS), as an emerging navigation and positioning continuously operating reference station system developed by combining Global Navigation Satellite System (GNSS) and network communication technology, has the advantages of fast, efficient, high-precision, networked and so on. It can not only measure the position and movement of the ground surface, but also monitor the changes of environmental parameters on the ground surface with the help of refractive and reflective features of GNSS signals. In this paper, we propose a multi-parameter integrated monitoring system of surface environment by using CORS station for “snow depth, soil moisture, atmospheric water vapor, and surface deformation”, which is used to expand the wide application of CORS station in ecological environment. Taking Qiqihar CORS station BFQE as an experimental case, firstly, the GNSS observation data (including SNR data), ephemeris data and meteorological data received by the CORS station during the experimental period are obtained and pre-processed; secondly, the non-linear least squares and Lomb-Scargle spectral analysis methods are applied to the re-sampled SNR data to decipher the shallow soil humidity and surface snow depth in the specific period. Then, the surface deformation sequence and atmospheric water vapor sequence of the station were obtained by the International GPS service for Geodynamics (IGS) using the relative positioning technique; finally, the correlation analysis was carried out by combining the results of the above surface environmental parameters to obtain the response relationship between the parameters. The experimental results show that the CORS station used for integrated monitoring of the surface environment can effectively monitor the temporal changes of multiple parameters, and the environmental parameters obtained from the inversion have a certain response relationship with each other. The atmospheric water vapor content affects the spatial and temporal distribution and intensity of rainfall, and the atmospheric water vapor inversion values are highly correlated with rainfall in trend. During the snowy period, the increase in atmospheric water vapor is accompanied by an increase in snow depth during snow accumulation periods. Rainfall formed by the increase of atmospheric water vapor is the main source of soil moisture, and the interpreted soil moisture always shows an increasing trend after heavy rainfall. The average correlation between soil moisture and measured data based on single stars is 0.75, and the correlation of the results of multi-satellite fusion interpretation reaches 0.89, and the root-mean-square error of soil moisture content is 0.87%. The surface deformation time series is stable in the north (N) and east (E) directions, and the deformation in the up (U) direction has some responsive fluctuations with atmospheric water vapor, snow depth and soil moisture.