Synthetic Aperture Radar (SAR) offers an all-weather alternative, and recent advances in deep generative models provide opportunities to reconstruct optical-like imagery directly from SAR data. In this study, we conduct a comparative evaluation of multiple generative adversarial network (GAN) architectures for translating SAR data into realistic red, green and blue (RGB) imagery in agricultural landscapes. The models were trained using ALOS-2/PALSAR-2 quad-polarimetric (quad-pol) data. A distinctive feature of our work is the evaluation of not only backscatter coefficients (Gamma nought) but also polarimetric parameters derived from quad-pol decompositions, including the generalised Freeman–Durden, H/A/Alpha, and Yamaguchi four-component methods. The comparative results show that paired image-to-image translation frameworks consistently outperform unpaired approaches. In particular, paired methods such as feature-guiding GAN and pix2pixHD, achieved high similarity to PlanetScope reference imagery, with mean structural similarity index values exceeding 0.98 across all SAR inputs. In contrast, unpaired approaches demonstrated more variable performance depending on the input features. Notably, PUT showed significant improvement when H/A/Alpha or Yamaguchi decompositions were used, whereas Freeman–Durden produced results comparable to Gamma nought. The performance gap between paired and unpaired frameworks was most evident in heterogeneous landscapes, such as areas with adjacent grasslands and forests. These findings demonstrate the effectiveness of GAN-based translation from polarimetric SAR to RGB imagery for agricultural monitoring. The integration of polarimetric information adds value to unpaired learning schemes, and the ability to generate optical-like imagery under challenging observation conditions has strong potential for practical use in crop monitoring and assessment.