images are often used as inputs for crop classification to fully account for the phenological status of crops [8; 9; 10; 11; 12; 13; 14; 15; 16]. However, an incomplete image time series is often available for early crop mapping to identify crop types at the early crop growth stage, which may achieve poor classification accuracy when compared to using a complete image time series [17]. Cloud contamination, common in optical imagery, is a major obstacle to obtaining cloud-free optical images at the dates optimal for early crop mapping. Synthetic-aperture radar (SAR) imagery, which can be acquired regardless of the weather conditions, can be used for crop mapping as an alternative to optical imagery [18; 19; 20; 21]. However, it may be challenging to achieve high classification accuracy comparable to optical imagery when specific crop types have similar physical structures but different spectral responses. Furthermore, the difficulty in the visual interpretation of SAR imagery compared with optical imagery is an obstacle to collecting sufficient training samples for supervised classification in inaccessible regions, such as in foreign countries. The joint use of SAR and optical imagery has often been employed to improve classification performance by synergizing complementarity between SAR and optical imagery [22; 23; 24; 25; 26]. However, the availability of cloud-free optical imagery at the critical crop growth stage for early crop mapping is still limited in the multi-sensor fusion approach. Regarding early-season crop mapping using remote sensing imagery, most previous studies have focused on the selection of multi-temporal image combinations [27; 28], the comparison of various classification methods [29], and the collection of reliable training samples [13; 30]. However, the difficulty in collecting cloud-free optical imagery at the optimal date for early crop mapping has not been fully explored. From a data availability perspective, the generation of cloud-free virtual optical images can be a feasible solution to overcome the missing problems in optical image acquisition for early crop mapping, which is the primary focus of this study. To this end, SAR imagery can be used as an important source of information for virtual optical image generation. Since both optical and SAR images have different imaging mechanisms and physical meanings, it is necessary to apply advanced methods for such a heterogeneous image translation [31]. Due to the great potential of deep learning (DL) for remote sensing image processing, many studies have attempted to generate virtual images using DL-based image-to-image translation. Convolutional neural networks (CNNs), which have been widely applied to image processing [32; 33], lack a coordination mechanism appropriate for multi-sensor image fusion [31] and manually design effective loss functions [34]. Meanwhile, inspired by [PERSON] et al. [35], conditional generative adversarial networks (CGANs) [34; 36] have been employed for virtual image generation in the remote sensing community because of their adversarial learning procedures suitable for image-to-image translation. The virtual reflectance of visible bands from other bands and satellites was generated using a CGAN-based prediction [37; 38; 39]. Regarding the optical image generation from SAR imagery, many studies have also conducted SAR-to-optical image translations based on CGANs, which have proven effective for virtual optical image generation [40; 41; 42; 43; 44]. Moreover, variants of CGAN, including cycle-consistent adversarial networks (CycleGANs) [45; 46; 47] and DualGANs [48], have also been proposed for image-to-image translation. The performance of the SAR-to-optical image translation depends on the effective extraction of the key features that account for the quantitative relationships between optical and SAR images. Thus, it is critical to quantify the different reflectance and scattering characteristics of various land-cover types.