快速无损准确监测作物氮素营养状况是精确农业中氮肥精确管理的前提与核心。日光诱导叶绿素荧光(Solar-induced chlorophyll fluorescence, SIF)能够直接、快速并灵敏的探测作物生理动态状况。本研究针对作物氮素营养监测精准的技术难题，将叶绿素荧光遥感探测原理与作物生理生态机制相结合，深入揭示不同氮素水平下小麦单叶和冠层2个尺度的SIF与氮素、色素、光合和叶绿素荧光动力学参数等的时空动态变化特征，着重阐明小麦SIF对氮素的光谱响应规律和机理关系，构建解释性和可靠性兼具的小麦氮素敏感SIF参数，并筛选能早期探测氮胁迫的最佳SIF参数，明确其动态特征和变化轨迹，最终确定最佳SIF参数在关键生育时期的生理阈值。预期结果为基于星-机-地平台叶绿素荧光的作物氮素精准探测和氮肥科学运筹提供理论基础和技术途径，对实现作物生长智能诊断和提升我国粮食生产能力具有重要意义。

In order to realize precision nitrogen management, it is critical to monitor crop nitrogen status accurately and non-destructively at different fertility levels. By unitizing the technology of solar-induced chlorophyll fluorescence (SIF), crop physiological and nutrient status can be directly detected sensitively at the levels of leaf, canopy and field during the early crop growth stage. On the basis of SIF theory and remote sensing, and adopting physiological and biochemical measurements and statistical analysis through combing the nondestructive monitoring technology with synchronously destructive sampling tests in wheat, this research concentrates on: (1) to reveal the spatial and temporal dynamic patterns and the quantitative relationships between chlorophyll fluorescence spectra (parameters), nitrogen, pigments, photosynthetic characters, fluorescence induction kinetic parameters at different leaf positions and canopy scales under varied nitrogen levels; (2) to clarify the responses of wheat chlorophyll fluorescence spectra (parameters) and underlying mechanisms at different nitrogen levels; (3) to extract the corresponding SIF parameters which are explanatory and sensitive to high and low nitrogen levels; (4) to identify specific SIF indices for early detection and diagnosis of the plant nitrogen stress; (5) to track the changes and dynamic characters for determining the physiological threshold of SIF parameters at the key stages under different nitrogen levels in wheat. The expected results will provide a theoretical and technological foundation for crop nitrogen status under varied nitrogen levels with SIF technology, which would support the satellite-unmanned aerial vehicle-ground platforms for accurate diagnosis of plant nitrogen status and scientific management of nitrogen fertilization, thus facilitating intelligent growth diagnosis and enhancing grain production capacity.