Rice crops are important in global food economy and are monitored by precise agricultural methods, in which crop morphology in high spatial resolution becomes the point of interest. Synthetic aperture radar (SAR) technology is being used for such agricultural purposes. Using polarimetric SAR (PolSAR) data, plant morphology dependent electromagnetic scattering models can be used to approximate the backscattering behaviors of the crops. However, the inversion of such models for the morphology estimation is complex, ill-posed, and computationally expensive. Here, a metamodel-based probabilistic inversion algorithm is proposed to invert the morphology-based scattering model for the crop biophysical parameter mainly focusing on the crop height estimation. The accuracy of the proposed approach is tested with ground measured biophysical parameters on rice fields in two different bands (X and C) and several channel combinations. Results show that in C-band the combination of the HH and VV channels has the highest overall accuracy through the crop growth cycle. Finally, the proposed metamodel-based probabilistic biophysical parameter retrieval algorithm allows estimation of rice crop height using PolSAR data with high accuracy and low computation cost. This research provides a new perspective on the use of PolSAR data in modern precise agriculture studies.