Nitrogen Sufficiency Index (NSI) is an important nitrogen (N) stress indicator for precision N management. It is usually calculated using variables such as leaf chlorophyll meter readings (SPAD) and vegetation indices (VIs). However, no consensus has been reached on the most preferred variable. Additionally, conventional NSI (NSI_uni) calculation assumes N being the sole yield-limiting factor, neglecting other factors such as soil water variability. To tackle these issues, this study compared various variables for NSI calculation and evaluated two new N stress indicators in minimizing the impact of confounding water treatment. The following ground- and aerial-derived variables were compared for NSI_uni calculation: SPAD, sampled leaf and canopy N content (LNC, CNC), LNC and CNC estimated using hyperspectral images acquired by an Unmanned Aerial Vehicle, and three VIs (Normalized Difference Vegetation Index (NDVI), Normalized Red Edge Index (NDRE), and Chlorophyll Index) from the hyperspectral images. Results demonstrated that ground-measured variables outperformed aerial-based variables in deriving N-responsive NSI. Especially, LNC derived NSI_uni responded to N treatment significantly in ten out of thirteen site-date datasets. For the second objective, a modified NSI (NSI_w) and the NDRE/NDVI ratio were compared to NSI_uni. NSI_w reduced water treatment effects in over 80% of the datasets where NSI_uni showed evident impacts. NDRE/NDVI performed similarly to NSI_w, with the notable advantage of not requiring prior knowledge of soil water spatial distribution. This research pioneers the optimization of N stress indicators by identifying the best variables for NSI and mitigating the effects of soil water variability. These advancements significantly contribute to precision N management in complex field conditions.

氮充足指数 （NSI） 是精确氮管理的重要氮 （N） 胁迫指标。它通常使用叶绿素仪表读数 （SPAD） 和植被指数 （VI） 等变量进行计算。但是，尚未就首选变量达成共识。此外，传统的 NSI （NSI_uni） 计算假设 N 是唯一的产量限制因素，而忽略了土壤水分变化等其他因素。为了解决这些问题，本研究比较了 NSI 计算的各种变量，并评估了两个新的 N 应力指标，以最大限度地减少混杂水处理的影响。比较了以下地面和航空衍生变量用于 NSI_uni 计算：SPAD、采样叶和冠层 N 含量 （LNC、CNC）、LNC 和 CNC，使用无人机获取的高光谱图像估计，以及来自高光谱图像的三个 VI（归一化差异植被指数 （NDVI）、归一化红边指数 （NDRE） 和叶绿素指数）。结果表明，在推导出 N 响应 NSI 方面，地面测量变量的性能优于基于航空的变量。特别是，LNC 衍生的 NSI_uni 在 13 个站点日期数据集中的 10 个数据集中对 N 处理有显着反应。对于第二个目标，将改良的 NSI （NSI_w） 和 NDRE/NDVI 比率与 NSI_uni 进行比较。NSI_在超过 80% 的数据集中减少了水处理效果，其中 NSI_uni 显示出明显的影响。NDRE/NDVI 的表现与 NSI_w 相似，具有显着优势，即不需要土壤水分空间分布的先验知识。 本研究通过确定 NSI 的最佳变量和减轻土壤水分变化的影响，开创了 N 胁迫指标的优化。这些进步极大地促进了复杂田间条件下的精确氮管理。