The RADARSAT Constellation Mission (RCM) performance evaluation is currently active for core Synthetic Aperture Radar (SAR) applications. This study aims to investigate the RCM performance for Soil Moisture Content (SMC) retrieval over bare soil. An initial performance evaluation is presented with focus on the Compact Polarimetric (CP) SAR imagery from the ScanSAR 30 m spatial resolution (SC30M) imaging mode. CP SAR images acquired over three Canadian experimental sites, equipped with Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) stations, are considered in our study. The attention is on RH and RV backscattering, which are the primary RCM CP products. The SMC retrieval is examined using the Random Forest Regression (RFR) machine learning approach. Results of our study indicate promising performance, which exceeds for the selected imaging mode the performance previously reported using simulated RCM data. A RFR retrieval algorithm was able to predict SMC with a correlation of 0.75 when compared to in-situ soil moisture measurements. A Root Mean Square Error (RMSE) = 5.9%, a bias = 4.6%, and an unbiased RMSE (ubRMSE) = 3.7% are achieved. A degradation in performance is reported for SMC retrieval under large radar incidence angles.