Hyperspectral remote sensing can quickly, nondestructively and accurately monitor crop water concentration and provide technical support for winter wheat growth monitoring, drought assessment, and variable irrigation. In this study, canopy spectral reflectance, leaf water concentration (LWC), leaf nitrogen concentration (LNC), leaf area index (LAI), and leaf dry matter (LDM) of four wheat cultivars were measured under different irrigation and nitrogen treatments, and the effects of nitrogen treatment and growth period on spectral reflectance and LWC were analyzed. Canopy spectral reflectance for different growth periods, irrigation, and nitrogen treatments showed significant changes, leading to the phenomena of “nitrogen treatment differentiation” and “growth period differentiation” for the normalized difference spectral index [NDSI (762, 1458, 2301)] and normalized difference infrared index (NDII) monitoring models. To reduce the influence of nitrogen treatment and growth period on the LWC estimation model, a modified normalized difference water index (mNDWI) was constructed by introducing the nitrogen factor (ratio of left and right peak area, RIDA) into the optimized combination of water-sensitive bands [ND (815, 1080), ND (1585, 1740), and ND (2030, 2260)]. Compared with NDSI (762, 1458, 2301), the R² of mNDWI was improved by 36.2%–41.1% under different nitrogen levels and 18.6%–22.4% in different growth periods; this effectively reduced the impact of nitrogen status on LWC monitoring and realized the unified modeling and accurate inversion of LWC for the entire growth period. The new index mNDWI, especially mNDWI (815, 1080) and mNDWI (2030, 2260), can effectively monitor the LWC status of wheat under different cultivation conditions, which is important for the real-time diagnosis of plant moisture to guide precision field irrigation applications.

高光谱遥感可以快速、无损、准确地监测作物水分含量，为冬小麦生长监测、干旱评估和变量灌溉提供技术支持。本研究测定了4个小麦品种在不同灌溉和施氮处理下的冠层光谱反射率、叶片水分浓度（LWC）、叶片氮浓度（LNC）、叶面积指数（LAI）和叶片干物质（LDM），以及分析了施氮处理和生长期对光谱反射率和LWC的影响。不同生育期、灌溉、施氮处理的冠层光谱反射率均有显着变化，导致归一化差分光谱指数出现“施氮分异”和“生长期分异”现象[NDSI (762, 1458, 2301)]和归一化差异红外指数（NDII）监测模型。为降低施氮处理和生长期对LWC估算模型的影响，通过将氮因子（左右峰面积比，RIDA）引入水-敏感带 [ND (815, 1080)、ND (1585, 1740) 和 ND (2030, 2260)]。与 NDSI（762、1458、2301）相比，R ND（1585、1740）和 ND（2030、2260）]。与 NDSI（762、1458、2301）相比，R ND（1585、1740）和 ND（2030、2260）]。与 NDSI（762、1458、2301）相比，RmNDWI ^{2在不同施氮水平下提高了36.2%~41.1%，在不同生育期提高了18.6%~22.4%；}有效降低了氮素状态对LWC监测的影响，实现了全生育期LWC的统一建模和准确反演。新指标mNDWI，尤其是mNDWI(815, 1080)和mNDWI(2030, 2260)，可以有效监测不同栽培条件下小麦的LWC状态，对于实时诊断植物水分，指导大田精准灌溉具有重要意义应用程序。