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In a complex monitoring environment, GNSS signals, which are extremely susceptible to environmental interference, can produce multipath errors, and the observation data contain a large number of poor observation values, resulting in reduced GNSS monitoring accuracy or even unavailability. Considering that the base station in the monitoring network is usually deployed in an open and unobstructed environment, and the same satellite information tracked by the base station and the monitoring station has a strong correlation, the method of identifying multipath errors in GNSS observation data based on prior information of base station's signal-to-noise ratio(SNR)was proposed. This method utilizes the strong correlation between satellite SNR observations and multipath errors, identifies poor data seriously affected by multipath through making inter-station differences in SNR observations, and removes them to resist the multipath effects of complex monitoring environment. Taking the monitoring environment of one landslide with serious occlusion in Sanmenxia area of Henan as an example, the verification based on the measured data shows that the proposed method can effectively identify the poor observation values with serious multipath effect affected by mountains, vegetation and artificial facilities, has stronger environmental adaptability, and significantly improves the fixed rate of ambiguity and positioning accuracy. Compared with the traditional fixed cut-off(TFC)model and the azimuth-dependent elevation mask(ADEM)model, the proposed method's ambiguity fixed rates are improved by the average of 39.6% and 28.6%, respectively; the positioning accuracy of the fixed solution is better than 4 mm in the E and N directions, and better than 9 mm in the U direction. [ABSTRACT FROM AUTHOR]