Remote sensing (RS) estimation of chlorophyll density serves as an effective measure to assess crop nitrogen (N) nutrition status and guide precision N fertilizer management. Throμgh multi-angular RS, this study aims to improve the estimation accuracy of chlorophyll density by reducing the disturbance of mixed background (soil and non-photosynthetic vegetation), and to explore the solutions to minimizing the influence of view zenith angles (VZAs). Wheat canopy multi-angular hyperspectral data (− 60°, − 45°, − 30°, 0°, 30°, 45°, 60°) were systematically collected throμgh three-years of field experiments. A soil non-photosynthetic background and angle insensitive vegetation index \(\left({\text{SAIVI}} = \frac{\Big({\left({\rho }_{750}\right)}^{-1} -{\left({\rho }_{860}\right)}^{-1}\Big)- \Big({\left({\rho }_{765}\right)}^{-1} -{\left({\rho }_{860}\right)}^{-1}\Big)}{\Big({\left({\rho }_{750}\right)}^{-1} -{\left({\rho }_{860}\right)}^{-1}\Big)+\Big({\left({\rho }_{765}\right)}^{-1} -{\left({\rho }_{860}\right)}^{-1}\Big)}\right)\) was proposed for inversion of chlorophyll density. Furthermore, SAIVI, along with another 11 vegetation indices (VIs), were evaluated for their performance in estimating three chlorophyll parameters, namely chlorophyll concentration (CC), canopy chlorophyll density based on leaf area (CCC_L) and canopy chlorophyll density based on fresh weight (CCC_W). The results indicated that SAIVI had strong stability in restraining distractor (mixed background of soil and non-photosynthetic vegetation). For inversion of CC, CCC_L and CCC_W, backward VZAs showed higher accuracy than vertical angle. The new proposed SAIVI performed best for estimating CCC_L and CCC_W with an optimal VZA of − 30°, and the corresponding R² and RRMSE of 0.76 and 0.77, 14.5% and 26.6%, respectively.

叶绿素密度的遥感 (RS) 估算是评估作物氮 (N) 营养状况和指导精准氮肥管理的有效措施。本研究旨在通过多角度遥感，通过减少混合背景（土壤和非光合植被）的干扰来提高叶绿素密度的估计精度，并探索最小化视角天顶角（VZAs）影响的解决方案。小麦冠层多角度高光谱数据（−60°、−45°、−30°、0°、30°、45°、60°）是通过三年的田间试验系统收集的。土壤非光合背景和角度不敏感的植被指数\(\left({\text{SAIVI}} = \frac{\Big({\left({\rho }_{750}\right)}^{-1} -{\left({\rho }_ {860}\right)}^{-1}\Big)- \Big({\left({\rho }_{765}\right)}^{-1} -{\left({\rho }_ {860}\right)}^{-1}\Big)}{\Big({\left({\rho }_{750}\right)}^{-1} -{\left({\rho } _{860}\right)}^{-1}\Big)+\Big({\left({\rho }_{765}\right)}^{-1} -{\left({\rho } _{860}\right)}^{-1}\Big)}\right)\)被提议用于叶绿素密度的反演。此外，还评估了 SAIVI 以及另外 11 个植被指数 (VI) 在估算三个叶绿素参数时的性能，即叶绿素浓度 (CC)、基于叶面积的冠层叶绿素密度 (CCC L) 和基于新鲜度的冠层_叶绿素密度重量（CCC _W). 结果表明，SAIVI 在抑制干扰物（土壤和非光合植被的混合背景）方面具有很强的稳定性。_{对于 CC、CCC L}和 CCC _W的反演，后向 VZA 显示出比垂直角更高的精度。新提出的 SAIVI 在估计 CCC _L和 CCC _W时表现最佳，最佳 VZA 为 − 30°，相应的 R ²和 RRMSE 分别为 0.76 和 0.77、14.5% 和 26.6%。