Observing rainfall intensity by measuring the rain-induced attenuation of microwave signals has become a new and inexpensive observation method. Since backhaul link is a very common and widely distributed microwave signal in cellular communication networks, rainfall can be monitored in a wide range with good spatial resolution by using backhaul links. The path-integrated rain attenuation can be retrieved by measuring the received signal level (RSL) of backhaul links, but its inherent shortcomings lead to large errors, thereby affecting the accuracy of the inversion of rain intensity. In this paper, we propose to measure the path-integrated rain attenuation based on estimating the signal-to-noise ratio (SNR) of received backhaul links by using the singular value decomposition (SVD) based SNR estimation and maximum likelihood (ML) based SNR estimation. With the usage of SNR estimation, we overcome the disadvantages of the former method, such as quantization error and zero level calibration, and strip the influence of noise from the received signal to more accurately measure the rain-induced attenuation. Numerical results are obtained from Monte Carlo simulations to compare the performance of different measurement methods. Different rain intensities lead to changes of SNR which is the most influential factor for both methods. Under the conditions of different SNR and rain intensity, comparisons are made between measurement methods based on measuring RSL and estimating SNR by two estimators. The mean square error is used to demonstrate the performance. The simulation results show that the proposed methods can measure the path-integrated rainfall more accurately in the case where SNR is relatively low.