Sugarcane lodging monitoring in large areas is a fundamental requirement for damage reduction, yield prediction, and loss evaluation. The polarimetric features extracted from polarimetric synthetic radar aperture have proved to be sensitive to wheat lodging, but for sugarcane lodging monitoring, there are three influences that make it more complicated: heterogeneity of sugarcane scattering, variations of sugarcane growing conditions, and sugarcane growth. A process was proposed to identify sugarcane lodging. First, object-based image segmentation was applied to extract the homogeneous sugarcane fields. Complex Wishart classifier was used to identify sugarcane fields from other land cover types, and 18 polarimetric features were extracted from six consecutive RADARSAT-2 datasets. Then, monthly variations of the polarimetric features were used to overcome the influence of variations of sugarcane growing conditions. We found that it was impossible to identify the lodged sugarcane based on the data from a single date. Finally, the influence of sugarcane growth was reduced by finding the polarimetric features, for which the monthly variations before and after lodging happened were opposite. This study indicated a great potential capability of C-band RADARSAT-2 data for monitoring sugarcane lodging.