It is necessary to monitor growth status of the crops due to continuous change of climate causing insecurity in crop cultivation. Low altitude remote sensing(LARS) system is utilized to accurately monitor the growth status of the crops. In this study, models for monitoring fresh weight(FW), one of the major growth factors of Chinese cabbage, were developed with structural indices and simple ratio calculated from bands in remotely sensed canopies by NIR, RE(imaging sensor A) and multispec-4c sensors(imaging sensor B) equipped with fixed-wing UAV depending on vegetation stages of normal planting(NP) and delayed planting(DP) Chinese cabbages. In results of imaging sensor A, the estimation models using structural indices and simple ratio were divided into NP and DP due to different attribute of reflectance in canopies with changed environment condition depending on different planting dates. The estimation models using simple ratio calculated by red edge and visible bands of NP showed better performance than other models, but RMSE was high. The models using simple ratio calculated by same bands of DP were feasible to accurately estimate FW(R2 of more than 0.946 with RMSE of less than 169.5 g). In results of imaging sensor B, the estimation models using structural indices and simple ratio on DP were divided into low to intermediate FW and intermediate to high FW. As a result, estimation models of all structural indices and simple ratio in low to intermediate FW bands were advisable to estimate FW(R2 of more than 0.860 with RMSE of less than 104.7 g). Estimation models of those calculated by red edge and visible bands in intermediate to high FW were only possible to accurately estimate FW(R2 of more than 0.532 with RMSE of less than 400.7 g).