Chinese cabbage has been an essential daily vegetable supply both for human and livestock meals in Asian countries as it has various nutrients and is easy to cultivate. However, as global climate changes and anthropogenic industrialization bring challenges to global food security, monitoring the growth status of Chinese cabbage from a macroscopic way can be of much importance to ensure its yield. Remote Sensing has a great potential to provide timely, ubiquitous, and frequent observations of the land surface at a large range of spatial scales, nevertheless the effects of shadows could also result in incorrect estimation of object properties such as reflectance, especially for the sensors of the middle or high-resolution satellite. A voxel-based simulation way has been a potential tool for compensating shadow effects which considers the shielding ratio of direct and diffuse solar irradiance categorized as Cast Shadow (CS) and Self Cast Shadow (SCS). In this study. six voxel-based virtual Chinese Cabbage farms with lengths and widths of 100m of different cultivation ways were simulated the reflectance of the red band (645-685nm) ranging from 0.045186 to 0.124521. Such result showed the available accessibility of this methodology for the comparison analysis with global available Sentinel-2 imagery with corresponding reflectance of fields in Kawakami Village, Nagano County, Japan. This study tends to provide benchmarks for monitoring different growth statuses of Chinese Cabbage at a large scale by high-resolution satellite images.