Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2) evaluate the potential of N status prediction across growth stages using the reflectance parameters acquired from the canopy sensor at an early growth stage; and, (3) investigate the prospect of combining the active canopy sensor and leaf fluorescence sensor data to estimate N nutrition index (NNI) indirectly using a general model across growth stages. The results indicated that data from both sensors were closely related to NNI across stages. However, using the direct NNI estimation method, among the tested indices, only the N balance index (NBI) could diagnose N status satisfactorily, based on the Kappa statistics. The effect of growth stages on proximal sensing was reduced by incorporating the information of days after sowing. It was found that the leaf fluorescence sensor performed relatively better in estimating plant N concentration whereas the canopy reflectance sensor performed better in aboveground biomass estimation. Their combination significantly improved the reliability of N diagnosis, including NNI prediction. In addition, the study confirmed that N status can be assessed by predicting aboveground biomass at the later stages using the canopy reflectance measurements at an early stage. Furthermore, the integrated NBI was verified to be a more robust and sensitive N status indicator than the chlorophyll concentration index. It is concluded that combining active canopy sensor data, of an early growth stage (e.g. V8), with leaf fluorescence sensor data, modified using days after sowing, can improve the accuracy of corn N status diagnosis across growth stages.

允许无损作物监测的快速方法对于准确的当季氮 (N) 状态评估和精确的氮管理是必不可少的。本文的目的是 (1) 比较叶片荧光传感器 Dualex 4 和主动冠层反射传感器 Crop Circle ACS-430 用于估计玉米 ( Zea maysL.) 跨越生长阶段的 N 个状态指标；(2) 使用在早期生长阶段从冠层传感器获取的反射参数评估整个生长阶段的 N 状态预测的潜力；(3) 研究结合活性冠层传感器和叶片荧光传感器数据以使用跨生长阶段的通用模型间接估计 N 营养指数 (NNI) 的前景。结果表明，来自两个传感器的数据在各个阶段都与 NNI 密切相关。然而，使用直接 NNI 估计方法，在测试的指标中，基于 Kappa 统计量，只有 N 平衡指数（NBI）可以令人满意地诊断 N 状态。通过结合播种后天数的信息，减少了生长阶段对近端感知的影响。结果发现，叶片荧光传感器在估算植物氮浓度方面表现相对较好，而冠层反射传感器在估算地上生物量方面表现较好。它们的组合显着提高了 N 诊断的可靠性，包括 NNI 预测。此外，该研究证实，可以通过使用早期的冠层反射率测量来预测后期的地上生物量来评估 N 状态。此外，综合 NBI 被证实是比叶绿素浓度指数更稳健和敏感的氮状态指标。得出的结论是，将早期生长阶段（例如 V8）的活性冠层传感器数据与在播种后使用几天修改的叶片荧光传感器数据相结合，可以提高玉米生长阶段的 N 状态诊断的准确性。