Active canopy sensors have been widely used in the studies of crop nitrogen (N) estimation as its suitability for different environmental conditions. Unmanned aerial vehicle (UAV) is a low-cost remote sensing platform for its great flexibility compared to traditional ways of remote sensing. UAV-based active canopy sensor is expected to take the advantages of both sides. The objective of this study is to determine whether UAV-based active canopy sensor has potential for monitoring rice N status, and to identify suitable models for practical use. Two field experiments were conducted with different N rates and varieties in 2016 and 2017. Plant sampling and sensing data collection were carried out at each key growth stage. Handheld sensing was conducted using a portable active canopy sensor RapidSCAN CS-45 with red, red edge and near infrared wavebands in all experiments for building prediction models. In the experiment of 2017, the sensor was mounted on a gimbal under a multi-rotor UAV to collect UAV-based data for validation. The results showed great potential of UAV-based active canopy sensor on rice leaf N status monitoring based on linear regression models, and red edge ratio vegetation index(RERVI) has good performance for predicting leaf dry matter (R2 = 0.76), leaf area index (R2 = 0.77) and leaf nitrogen accumulation (R2 = 0.79). UAV-based sensing with 1.5 m height above rice canopy is suitable for practical use. For better estimation, research on better prediction models and widely-feasible operational mode are needed in the future study.