Dynamic assessment of heavy metal contamination in crops is essential for food security and the farmland ecological environment. A new index for monitoring heavy metal stress based on the assimilation of synthetic aperture radar (SAR) data and the crop growth model is performed. The improved World Food Study (WOFOST) model was used in this study, which is embedded with two stress factors to improve the accuracy of assimilation. Biomass (BM) values retrieved by SAR data were assimilated into the improved WOFOST model to simulate dry weight of rice roots (WRT), and the root mass ratio (RMR, WRT/BM) was calculated as an index for monitoring heavy metal stress. SAR shows enormous potential for monitoring crop growth status in cloudy area. Compared with other physiological indices, RMR could weaken the weight change of rice caused by other background factors. In the temporal scale, RMR showed a faster significant decrease when the stress was greater. The spatial distribution of RMR and the stress factors exhibited good consistency. These results suggest that RMR derived from the assimilation method based on SAR data and the improved WOFOST model is effective for dynamically monitoring the rice growth status in cloudy regions under heavy metal stress.